{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bcf265ed-2f3f-47c2-97f3-c3190e9be156",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\A\\Desktop\n",
      "                  2021年    2020年    2019年    2018年    2017年    2016年    2015年  \\\n",
      "指标                                                                              \n",
      "电力可供量(亿千瓦小时)    85200.1  77620.2  74866.3  71509.2  65914.0  61204.4  58021.3   \n",
      "电力生产量(亿千瓦小时)    85342.5  77790.6  75034.3  71661.3  66044.5  61331.6  58145.7   \n",
      "水电生产电力量(亿千瓦小时)  13390.0  13552.1  13044.4  12317.9  11978.7  11840.5  11302.7   \n",
      "火电生产电力量(亿千瓦小时)  58058.7  53302.5  52201.5  50963.2  47546.0  44370.7  42841.9   \n",
      "核电生产电力量(亿千瓦小时)   4075.2   3662.5   3483.5   2943.6   2480.7   2132.9   1707.9   \n",
      "\n",
      "                  2014年    2013年    2012年    2011年    2010年    2009年    2008年  \\\n",
      "指标                                                                              \n",
      "电力可供量(亿千瓦小时)    57830.5  54204.1  49767.7  47002.7  41936.5  37032.7  34540.8   \n",
      "电力生产量(亿千瓦小时)    57944.6  54316.4  49875.5  47130.2  42071.6  37146.5  34668.8   \n",
      "水电生产电力量(亿千瓦小时)  10728.8   9202.9   8721.1   6989.5   7221.7   6156.4   5851.9   \n",
      "火电生产电力量(亿千瓦小时)  44001.1  42470.1  38928.1  38337.0  33319.3  29827.8  27900.8   \n",
      "核电生产电力量(亿千瓦小时)   1325.4   1116.1    973.9    863.5    738.8    701.3    683.9   \n",
      "\n",
      "                  2007年    2006年    2005年    2004年  \n",
      "指标                                                  \n",
      "电力可供量(亿千瓦小时)    32712.4  28588.4  24940.8  21972.3  \n",
      "电力生产量(亿千瓦小时)    32815.5  28657.3  25002.6  22033.1  \n",
      "水电生产电力量(亿千瓦小时)   4852.6   4357.9   3970.2   3535.4  \n",
      "火电生产电力量(亿千瓦小时)  27229.3  23696.0  20473.4  17955.9  \n",
      "核电生产电力量(亿千瓦小时)    621.3    548.4    530.9    504.7  \n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "os.chdir(r'C:\\Users\\A\\Desktop')\n",
    "print(os.getcwd())\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df1 = pd.read_csv('Electricity_20_bfill.csv',index_col = 0)\n",
    "print(df1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e77f52e1-6208-4778-9988-3ff0eabef2cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 203.42823168489096\n",
      "R^2 Score: 0.9999994131817657\n"
     ]
    },
    {
     "data": {
      "image/png": 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2ivnjjz+MntesWRMNGzZEQUHBPb+vOaKjo6FWqzFjxgyT/7GKiJJXu3bt4OHhgUWLFhmNYVxc3N+u4O3h4YEuXbrgq6++wrlz50zew+BOP7+KyPF+mZtjmzZtEBwcjDlz5pjkZM5YWMLccS9LjRo17vje9vb2GDhwIFauXIm4uDi0aNHC7MPI9yMiIgIajQaffvqpUf6LFy9GXl4eoqKiAFj2OTAUQ9u3b1fadDodvvjiC6M4a//c1Go1unfvju+//95ouYPs7Gx888036Ny5M1xcXO66Dao4nGkim/vhhx9w7do1PP3002X2P/LII8r/Zvv3749JkyZh9erVeOaZZzB8+HC0bdsWV65cwQ8//IBFixahVatWaNCgAdzc3LBo0SLUqlULNWrUQGhoKIKDgzFixAisXr0aPXr0wLPPPovTp0/j66+/Vn5pGjz55JOIj49Hnz59EBUVhfT0dCxatAhNmzbF9evX73l/+/fvj+eeew4LFixAZGSkycmkTZs2RdeuXdG2bVvUqVMHBw4cwOrVq/Hyyy/f83uao0GDBnjvvfcwZcoUnD17Fr1790atWrWQnp6O7777DiNHjsRrr70GBwcHvPfee3jppZfw+OOPo3///khPT8eSJUvMOk/k008/RefOndGmTRuMHDkSwcHBOHv2LNavX6/cQqNt27YAgDfffBMDBgyAg4MDnnrqqQrL0eDChQv4+uuvTdpr1qxpUuxaOo52dnZYuHAhnnrqKbRu3RrDhg2Dj48PTpw4gWPHjmHTpk1GY/Hqq68iMjISarUaAwYMMHsfDMwZ97K0bdsWCxcuxHvvvYeGDRvC09PTaNbm+eefx6effootW7bggw8+sDive+Hh4YEpU6ZgxowZ6NGjB55++mmkpaVhwYIFaN++vXIiuiWfg2bNmuGRRx7BlClTcOXKFdSpUwcrVqxAcXGxUVx5/Nzee+89JCQkoHPnzhgzZgzs7e3x+eefo6CgALNnz7b28NH9qNiL9YhMPfXUU+Lo6Cg3bty4Y8zQoUPFwcFBLl++LCIif/zxh7z88stSr1490Wg04ufnJ0OGDFH6RUS+//57adq0qdjb25tcfv3xxx9LvXr1RKvVSqdOneTAgQMmSw6UlJTI+++/L4GBgaLVauXhhx+WdevWyZAhQyQwMNAoP5ix5IBBfn6+ODk5CQD5+uuvTfrfe+896dChg7i5uYmTk5OEhITIzJkzpbCw8K7bNVwavWrVqrvGGS6Rv3TpUpn9a9askc6dO0uNGjWkRo0aEhISIjExMZKWlmYUt2DBAgkODhatVivt2rWT7du3m4xhWZe+i4gcPXpU+vTpI25ubuLo6ChNmjSRt99+2yjm3XfflXr16omdnZ3JpeDWzPFO7rbkQOmf/+1LDlia486dO+Uf//iH1KpVS2rUqCEtW7aUzz77TOkvLi6WV155RTw8PESlUinvZRjbDz/80OS973Xcy7rsPisrS6KioqRWrVp3XK6hWbNmYmdnJ+fPn7/bkJrkV1bupRnyMSyXcbt58+ZJSEiIODg4iJeXl4wePdpkGQAR8z8Hp0+floiICNFqteLl5SX//Oc/JSEhwWjJAYN7/bmJlP374uDBgxIZGSk1a9YUZ2dneeyxx2T37t1mjcftyyJQ+VGJ8MwxIiK6dw8//DDq1KmDxMREW6dCVK54ThMREd2zAwcOICUlBc8//7ytUyEqd5xpIiIiix09ehTJycn4+OOPcfnyZZw5c4Y3oqUHHmeaiIjIYqtXr8awYcNQVFSE5cuXs2CiaoEzTURERERm4EwTERERkRlYNBERERGZgYtbWklJSQkuXryIWrVqWeW2B0RERFT+RATXrl2Dr6/v3943kUWTlVy8eJH3ByIiIqqiMjIy4Ofnd9cYFk1WUqtWLQD6Qed9goiIiKqG/Px8+Pv7K9/jd8OiyUoMh+RcXFxYNBEREVUx5pxawxPBiYiIiMzAoomIiIjIDCyaiIiIiMzAoomIiIjIDCyaiIiIiMzAoomIiIjIDCyaiIiIiMzAoomIiIjIDCyaiIiIiMzAFcGJiIioUtPpgB07gMxMwMcHCA8H1OqKz4NFExEREVVa8fHA2LHA+fN/tfn5AXPnAtHRFZsLD88RERFRpRQfD/TrZ1wwAcCFC/r2+PiKzYdFExEREVU6Op1+hknEtM/QNm6cPq6isGgiIiKiSmfHDtMZptJEgIwMfVxFYdFERERElU5mpnXjrIFFExEREVU6Pj7WjbMGFk1ERERU6YSH66+SU6nK7lepAH9/fVxFYdFERERElY5arV9WADAtnAzP58yp2PWaWDQRERFRpRQdDaxeDdSrZ9zu56dvr+h1mri4JREREVVa0dFAr15cEZyIiIjob6nVQNeuts6Ch+eIiIiIzMKiiYiIiMgMLJqIiIiIzMCiiYiIiMgMLJqIiIiIzMCiiYiIiMgMLJqIiIiIzMCiiYiIiMgMLJqIiIiIzMCiiYiIiMgMLJqIiIiIzMCiiYiIiMgMLJqIiIiIzMCiiYiIiMgMLJqIiIiIzMCiiYiIiMgMLJqIiIiIzGDTokmn0+Htt99GcHAwnJyc0KBBA7z77rsQESVGRDB16lT4+PjAyckJEREROHXqlNF2rly5gkGDBsHFxQVubm544YUXcP36daOYw4cPIzw8HI6OjvD398fs2bNN8lm1ahVCQkLg6OiIFi1aYMOGDeWz40RERFTl2LRo+uCDD7Bw4ULMmzcPqamp+OCDDzB79mx89tlnSszs2bPx6aefYtGiRdi7dy9q1KiByMhI3Lp1S4kZNGgQjh07hoSEBKxbtw7bt2/HyJEjlf78/Hx0794dgYGBSE5Oxocffojp06fjiy++UGJ2796NgQMH4oUXXsCvv/6K3r17o3fv3jh69GjFDAYRERFVbmJDUVFRMnz4cKO26OhoGTRokIiIlJSUiLe3t3z44YdKf25urmi1Wlm+fLmIiBw/flwAyP79+5WYn376SVQqlVy4cEFERBYsWCC1a9eWgoICJeb111+XJk2aKM+fffZZiYqKMsolNDRUXnrpJbP2JS8vTwBIXl6eWfFERERke5Z8f9t0pqljx45ITEzEyZMnAQCHDh3Czp070bNnTwBAeno6srKyEBERobzG1dUVoaGhSEpKAgAkJSXBzc0N7dq1U2IiIiJgZ2eHvXv3KjFdunSBRqNRYiIjI5GWloarV68qMaXfxxBjeJ/bFRQUID8/3+hBREREDy57W775G2+8gfz8fISEhECtVkOn02HmzJkYNGgQACArKwsA4OXlZfQ6Ly8vpS8rKwuenp5G/fb29qhTp45RTHBwsMk2DH21a9dGVlbWXd/ndrGxsZgxY8a97DYRERFVQTadaVq5ciWWLVuGb775BgcPHsTSpUvx0UcfYenSpbZMyyxTpkxBXl6e8sjIyLB1SkRERFSObDrTNGnSJLzxxhsYMGAAAKBFixb4/fffERsbiyFDhsDb2xsAkJ2dDR8fH+V12dnZaN26NQDA29sbOTk5RtstLi7GlStXlNd7e3sjOzvbKMbw/O9iDP2302q10Gq197LbREREVAXZdKbpzz//hJ2dcQpqtRolJSUAgODgYHh7eyMxMVHpz8/Px969exEWFgYACAsLQ25uLpKTk5WYzZs3o6SkBKGhoUrM9u3bUVRUpMQkJCSgSZMmqF27thJT+n0MMYb3ISIiomquAk5Mv6MhQ4ZIvXr1ZN26dZKeni7x8fHi7u4ukydPVmJmzZolbm5u8v3338vhw4elV69eEhwcLDdv3lRievToIQ8//LDs3btXdu7cKY0aNZKBAwcq/bm5ueLl5SWDBw+Wo0ePyooVK8TZ2Vk+//xzJWbXrl1ib28vH330kaSmpsq0adPEwcFBjhw5Yta+8Oo5IiKiqseS72+bFk35+fkyduxYCQgIEEdHR6lfv768+eabRksDlJSUyNtvvy1eXl6i1WqlW7dukpaWZrSdP/74QwYOHCg1a9YUFxcXGTZsmFy7ds0o5tChQ9K5c2fRarVSr149mTVrlkk+K1eulMaNG4tGo5FmzZrJ+vXrzd4XFk1ERERVjyXf3yqRUstv0z3Lz8+Hq6sr8vLy4OLiYut0iIiIyAyWfH/b9ERwIiIish6dDtixA8jMBHx8gPBwQK22dVYPDhZNRERED4D4eGDsWOD8+b/a/PyAuXOB6Gjb5fUgsenVc0RERHT/4uOBfv2MCyYAuHBB3x4fb5u8HjQsmoiIiKownU4/w1TWGcqGtnHj9HF0f1g0ERERVWE7dpjOMJUmAmRk6OPo/rBoIiIiqsIyM60bR3fGoomIiKgKK3WXMavE0Z2xaCIiIqrCwsP1V8mpVGX3q1SAv78+ju4PiyYiIqIqTK3WLysAmBZOhudz5nC9Jmtg0URERFTFRUcDq1cD9eoZt/v56du5TpN1cHFLIiKiB0B0NNCrF1cEL08smoiIiB4QajXQtauts3hw8fAcERERkRlYNBERERGZgUUTERERkRl4ThMREZGN6HQ8cbsqYdFERERkA/Hx+hvtlr5vnJ+ffs0lLhFQOfHwHBERUQWLjwf69TO90e6FC/r2+Hjb5EV3x6KJiIioAul0+hkmEdM+Q9u4cfo4qlxYNBEREVWgHTtMZ5hKEwEyMvRxVLmwaCIiIqpAmZnWjaOKw6KJiIioAvn4WDeOKg6LJiIiogoUHq6/Sk6lKrtfpQL8/fVxVLmwaCIiIqpAarV+WQHAtHAyPJ8zh+s1VUYsmoiIiCpYdDSwejVQr55xu5+fvp3rNFVOXNySiIjIBqKjgV69uCJ4VcKiiYiIyEbUaqBrV1tnQebi4TkiIiIiM7BoIiIiIjIDiyYiIiIiM7BoIiIiIjIDiyYiIiIiM7BoIiIiIjIDlxwgIiK6A52O6yjRX1g0ERERlSE+Hhg7Fjh//q82Pz/9LVC4Ynf1xMNzREREt4mPB/r1My6YAODCBX17fLxt8iLbYtFERERUik6nn2ESMe0ztI0bp4+j6oVFExERUSk7dpjOMJUmAmRk6OOoemHRREREVEpmpnXj6MHBoomIiKgUHx/rxtGDg0UTERFRKeHh+qvkVKqy+1UqwN9fH0fVC4smIiKiUtRq/bICgGnhZHg+Zw7Xa6qObFo0BQUFQaVSmTxiYmIAALdu3UJMTAzq1q2LmjVrom/fvsjOzjbaxrlz5xAVFQVnZ2d4enpi0qRJKC4uNorZunUr2rRpA61Wi4YNGyIuLs4kl/nz5yMoKAiOjo4IDQ3Fvn37ym2/iYiocouOBlavBurVM27389O3c52m6smmRdP+/fuRmZmpPBISEgAAzzzzDABg/Pjx+PHHH7Fq1Sps27YNFy9eRHSpT6pOp0NUVBQKCwuxe/duLF26FHFxcZg6daoSk56ejqioKDz22GNISUnBuHHjMGLECGzatEmJ+fbbbzFhwgRMmzYNBw8eRKtWrRAZGYmcnJwKGgkiIqpsoqOBs2eBLVuAb77R/5mezoKpWpNKZOzYsdKgQQMpKSmR3NxccXBwkFWrVin9qampAkCSkpJERGTDhg1iZ2cnWVlZSszChQvFxcVFCgoKRERk8uTJ0qxZM6P36d+/v0RGRirPO3ToIDExMcpznU4nvr6+Ehsba3bueXl5AkDy8vIs22kiIiKyGUu+vyvNOU2FhYX4+uuvMXz4cKhUKiQnJ6OoqAgRERFKTEhICAICApCUlAQASEpKQosWLeDl5aXEREZGIj8/H8eOHVNiSm/DEGPYRmFhIZKTk41i7OzsEBERocSUpaCgAPn5+UYPIiIienBVmqJp7dq1yM3NxdChQwEAWVlZ0Gg0cHNzM4rz8vJCVlaWElO6YDL0G/ruFpOfn4+bN2/i8uXL0Ol0ZcYYtlGW2NhYuLq6Kg9/f3+L95mIiIiqjkpTNC1evBg9e/aEr6+vrVMxy5QpU5CXl6c8MjIybJ0SERERlSN7WycAAL///jt++eUXxJe6A6K3tzcKCwuRm5trNNuUnZ0Nb29vJeb2q9wMV9eVjrn9irvs7Gy4uLjAyckJarUaarW6zBjDNsqi1Wqh1Wot31kiIiKqkirFTNOSJUvg6emJqKgopa1t27ZwcHBAYmKi0paWloZz584hLCwMABAWFoYjR44YXeWWkJAAFxcXNG3aVIkpvQ1DjGEbGo0Gbdu2NYopKSlBYmKiEkNERERk85mmkpISLFmyBEOGDIG9/V/puLq64oUXXsCECRNQp04duLi44JVXXkFYWBgeeeQRAED37t3RtGlTDB48GLNnz0ZWVhbeeustxMTEKLNAo0aNwrx58zB58mQMHz4cmzdvxsqVK7F+/XrlvSZMmIAhQ4agXbt26NChA+bMmYMbN25g2LBhFTsYREREVHlVwNV8d7Vp0yYBIGlpaSZ9N2/elDFjxkjt2rXF2dlZ+vTpI5mZmUYxZ8+elZ49e4qTk5O4u7vLxIkTpaioyChmy5Yt0rp1a9FoNFK/fn1ZsmSJyXt99tlnEhAQIBqNRjp06CB79uyxaD+45AAREVHVY8n3t0pExMZ12wMhPz8frq6uyMvLg4uLi63TISIiIjNY8v1dKc5pIiIiIqrsWDQRERERmYFFExEREZEZWDQRERERmYFFExEREZEZWDQRERERmYFFExEREZEZWDQRERERmYFFExEREZEZbH7vOSIiqr50OmDHDiAzE/DxAcLDAbXa1lkRle2eiqbExEQkJiYiJycHJSUlRn1fffWVVRIjIqIHW3w8MHYscP78X21+fsDcuUB0tO3yIroTiw/PzZgxA927d0diYiIuX76Mq1evGj2IiIj+Tnw80K+fccEEABcu6Nvj422TF9HdWHzDXh8fH8yePRuDBw8ur5yqJN6wl4jIPDodEBRkWjAZqFT6Gaf0dB6qo/JXrjfsLSwsRMeOHe85OSIiqt527LhzwQQAIkBGhj6OqDKxuGgaMWIEvvnmm/LIhYiIqoHMTOvGEVUUi08Ev3XrFr744gv88ssvaNmyJRwcHIz6//Wvf1ktOSIievD4+Fg3jqiiWFw0HT58GK1btwYAHD161KhPpVJZJSkiInpwhYfrz1m6cEF/KO52hnOawsMrPjeiu7G4aNqyZUt55EFERNWEWq1fVqBfP32BVLpwMvzfe84cngROlc99rQh+/vx5nL/b2XxERERliI4GVq8G6tUzbvfz07dznSaqjCwumkpKSvDOO+/A1dUVgYGBCAwMhJubG959912ThS6JiIjuJDoaOHsW2LIF+OYb/Z/p6SyYqPKy+PDcm2++icWLF2PWrFno1KkTAGDnzp2YPn06bt26hZkzZ1o9SSIiejCp1UDXrrbOgsg8Fi9u6evri0WLFuHpp582av/+++8xZswYXLhwwaoJVhVc3JKIiKjqKdfFLa9cuYKQkBCT9pCQEFy5csXSzRERERFVCRYXTa1atcK8efNM2ufNm4dWrVpZJSkiIiKiysbic5pmz56NqKgo/PLLLwgLCwMAJCUlISMjAxs2bLB6gkRERESVgcUzTY8++ihOnjyJPn36IDc3F7m5uYiOjkZaWhrCuRIZERERPaAsPhGcysYTwYmIiKoeS76/zTo8d/jwYTRv3hx2dnY4fPjwXWNbtmxpfqZEREREVYRZRVPr1q2RlZUFT09PtG7dGiqVCmVNUKlUKuh0OqsnSURERGRrZhVN6enp8PDwUP5OREREVN2YVTQFBgYqf//999/RsWNH2Nsbv7S4uBi7d+82iiUiIiJ6UFh89dxjjz1W5iKWeXl5eOyxx6ySFBEREVFlY3HRJCJQqVQm7X/88Qdq1KhhlaSIiIiIKhuzF7eM/t9tp1UqFYYOHQqtVqv06XQ6HD58GB07drR+hkRERESVgNlFk6urKwD9TFOtWrXg5OSk9Gk0GjzyyCN48cUXrZ8hERGVC50O2LEDyMwEfHyA8HBArbZ1VkSVl9lF05IlSwAAQUFBmDRpEpydncstKSIiKl/x8cDYscD583+1+fkBc+cC/zuwQES3sficpueffx4XLlwwaT916hTOnj1rjZyIiKgcxccD/foZF0wAcOGCvj0+3jZ5EVV2FhdNQ4cOxe7du03a9+7di6FDh1ojJyIiKic6nX6GqawbaBnaxo3TxxGRMYuLpl9//RWdOnUyaX/kkUeQkpJijZyIiKic7NhhOsNUmgiQkaGPIyJjFhdNKpUK165dM2nPy8vjLVSIiCq5zEzrxhFVJxYXTV26dEFsbKxRgaTT6RAbG4vOnTtbNTkiIrIuHx/rxhFVJ2ZfPWfwwQcfoEuXLmjSpAnCw8MBADt27EB+fj42b95s9QSJiMh6wsP1V8lduFD2eU0qlb7/f7/eiagUi2eamjZtisOHD+PZZ59FTk4Orl27hueffx4nTpxA8+bNLU7gwoULeO6551C3bl04OTmhRYsWOHDggNIvIpg6dSp8fHzg5OSEiIgInDp1ymgbV65cwaBBg+Di4gI3Nze88MILuH79ulHM4cOHER4eDkdHR/j7+2P27NkmuaxatQohISFwdHREixYtsGHDBov3h4ioMlOr9csKAPoCqTTD8zlzuF4TUZnEhq5cuSKBgYEydOhQ2bt3r5w5c0Y2bdokv/32mxIza9YscXV1lbVr18qhQ4fk6aefluDgYLl586YS06NHD2nVqpXs2bNHduzYIQ0bNpSBAwcq/Xl5eeLl5SWDBg2So0ePyvLly8XJyUk+//xzJWbXrl2iVqtl9uzZcvz4cXnrrbfEwcFBjhw5Yta+5OXlCQDJy8uzwsgQEZWvNWtE/PxE9PNN+oe/v76dqDqx5PtbJVLWBO2dbd++/a79Xbp0MXtbb7zxBnbt2oUdd7hMQ0Tg6+uLiRMn4rXXXgOgP+Hcy8sLcXFxGDBgAFJTU9G0aVPs378f7dq1AwBs3LgRTzzxBM6fPw9fX18sXLgQb775JrKysqDRaJT3Xrt2LU6cOAEA6N+/P27cuIF169Yp7//II4+gdevWWLRo0d/uS35+PlxdXZGXlwcXFxezx4CIyFa4IjiRZd/fFp/T1LVrV5O20jfwteQKuh9++AGRkZF45plnsG3bNtSrVw9jxoxRbseSnp6OrKwsREREKK9xdXVFaGgokpKSMGDAACQlJcHNzU0pmAAgIiICdnZ22Lt3L/r06YOkpCR06dJFKZgAIDIyEh988AGuXr2K2rVrIykpCRMmTDDKLzIyEmvXri0z94KCAhQUFCjP8/Pzzd5vIqLKQK0GyviVTkR3YPE5TVevXjV65OTkYOPGjWjfvj1+/vlni7Z15swZLFy4EI0aNcKmTZswevRovPrqq1i6dCkAICsrCwDg5eVl9DovLy+lLysrC56enkb99vb2qFOnjlFMWdso/R53ijH03y42Nhaurq7Kw9/f36J9JyIioqrF4pkmw417S/vHP/4BjUaDCRMmIDk52extlZSUoF27dnj//fcBAA8//DCOHj2KRYsWYciQIZamVqGmTJliNDOVn5/PwomIiOgBZvFM0514eXkhLS3Notf4+PigadOmRm0PPfQQzp07BwDw9vYGAGRnZxvFZGdnK33e3t7Iyckx6i8uLsaVK1eMYsraRun3uFOMof92Wq0WLi4uRg8iIiJ6cFlcNB0+fNjocejQIWzcuBGjRo1C69atLdpWp06dTAqtkydPIjAwEAAQHBwMb29vJCYmKv35+fnYu3cvwsLCAABhYWHIzc01muHavHkzSkpKEBoaqsRs374dRUVFSkxCQgKaNGmC2rVrKzGl38cQY3gfIiIiquYsvTRPpVKJnZ2dqFQqo0dYWJikpqZatK19+/aJvb29zJw5U06dOiXLli0TZ2dn+frrr5WYWbNmiZubm3z//fdy+PBh6dWrV5lLDjz88MOyd+9e2blzpzRq1MhoyYHc3Fzx8vKSwYMHy9GjR2XFihXi7OxssuSAvb29fPTRR5KamirTpk3jkgNEREQPOEu+vy0ums6ePWv0OHfunFEBY6kff/xRmjdvLlqtVkJCQuSLL74w6i8pKZG3335bvLy8RKvVSrdu3SQtLc0o5o8//pCBAwdKzZo1xcXFRYYNGybXrl0zijl06JB07txZtFqt1KtXT2bNmmWSy8qVK6Vx48ai0WikWbNmsn79erP3g0UTEZWH4mKRLVtEvvlG/2dxsa0zInqwlOs6TVQ2rtNERNak0wEzZ+pX775y5a92Pz99W3S07XIjepBYfZ2mTz/91Ow3f/XVV82OJSIiU/HxwMiRwB9/mPZduAD06wesXs3CiaiimTXTFBwcbPT80qVL+PPPP+Hm5gYAyM3NhbOzMzw9PXHmzJlySbSy40wTEVlDfLy+KLrbb2bDTXXT07mCN9H9suT726yr59LT05XHzJkz0bp1a6SmpuLKlSu4cuUKUlNT0aZNG7z77rtW2QEioupIpwPGjr17wQTo+zMy9LdAIaKKY/GSA2+//TY+++wzNGnSRGlr0qQJPvnkE7z11ltWTY6IqDrZsQM4f978+MzM8suFiExZXDRlZmaiuLjYpF2n05ksDklEROaztAjy8SmfPIiobBYXTd26dcNLL72EgwcPKm3JyckYPXq00Y11iYjIMpYUQf7+QHh4+eVCRKYsLpq++uoreHt7o127dtBqtdBqtejQoQO8vLzw5ZdflkeORETVQni4/gRvlervY+fM4UngRBXN4hv2enh4YMOGDTh58iRSU1OhUqkQEhKCxo0bl0d+RETVhlqtX4OpXz994VTWCeF16wJffMHlBohs4b4WtzS8VGXOf4secFxygIisJT5efxVd6ZPC69YFXn0VePNNzjARWZPVlxy43X/+8x+0aNECTk5OcHJyQsuWLfHf//73npIlIiJj0dHA2bPAli3AN9/o/8zOBqZOZcFEZEsWH57717/+hbfffhsvv/wyOnXqBADYuXMnRo0ahcuXL2P8+PFWT5KIqLpRq4GuXW2dBRGVZvHhueDgYMyYMQPPP/+8UfvSpUsxffp0pKenWzXBqoKH54iIiKqecj08l5mZiY4dO5q0d+zYEZlcaY2IiIgeUBYXTQ0bNsTKlStN2r/99ls0atTIKkkRERERVTYWn9M0Y8YM9O/fH9u3b1fOadq1axcSExPLLKaIiIiIHgQWzzT17dsX+/btg7u7O9auXYu1a9fC3d0d+/btQ58+fcojRyIiIiKbs2imqaioCC+99BLefvttfP311+WVExEREVGlY9FMk4ODA9asWVNeuRARERFVWhYfnuvduzfWrl1bDqkQERERVV4WnwjeqFEjvPPOO9i1axfatm2LGjVqGPW/+uqrVkuOiKgy0umAHTuAzEzAx0d/o12u1E304LunxS3vuDGVCmfOnLnvpKoiLm5JVD2UdV84Pz/9jXZ5E12iqseS72+LZ5qq64rfRETx8UC/fsDt/9W8cEHfvno1CyeiB5lFM0179uzBjz/+iMLCQnTr1g09evQoz9yqFM40ET3YdDogKMh4hqk0lUo/45SezkN1RFVJudxGZfXq1ejUqRPmzp2LL7/8ElFRUfjoo4/uO1kioqpgx447F0yAfvYpI0MfR0QPJrOLptjYWLz44ovIy8vD1atX8d577+H9998vz9yIiCoNc2+tyVtwEj24zC6a0tLS8Nprr0H9v3nniRMn4tq1a8jJySm35IiIKgsfH+vGEVHVY3bR9Oeffxod69NoNHB0dMT169fLJTEiosokPFx/zpJKVXa/SgX4++vjiOjBZNHVc19++SVq1qypPC8uLkZcXBzc3d2VNq7TREQPIrVav6xAv376Aqn0JTSGQmrOHJ4ETvQgM/vquaCgIKju9F8sw8a4ThOvniN6wJW1TpO/v75g4nIDRFWPJd/fFi9uSWVj0URUfXBFcKIHR7kubklEVN2p1UDXrrbOgogqmsU37CUiIiKqjlg0EREREZmBRRMRERGRGVg0EREREZnBrBPB8/Pzzd4grxwjIiKiB5FZRZObm9vfrtFkoNPp7ishIiIiosrIrKJpy5Ytyt/Pnj2LN954A0OHDkVYWBgAICkpCUuXLkVsbGz5ZElERERkYxYvbtmtWzeMGDECAwcONGr/5ptv8MUXX2Dr1q3WzK/K4OKWREREVY8l398WnwielJSEdu3ambS3a9cO+/bts3RzRERERFWCxUWTv78//v3vf5u0f/nll/D397dKUkRERESVjcW3Ufnkk0/Qt29f/PTTTwgNDQUA7Nu3D6dOncKaNWusniARERFRZWDxTNMTTzyBkydP4qmnnsKVK1dw5coVPPXUUzh58iSeeOIJi7Y1ffp0qFQqo0dISIjSf+vWLcTExKBu3bqoWbMm+vbti+zsbKNtnDt3DlFRUXB2doanpycmTZqE4uJio5itW7eiTZs20Gq1aNiwIeLi4kxymT9/PoKCguDo6IjQ0FAeaiSqxHQ6YOtWYPly/Z+8aJeIKsI93bDX398f77//vlUSaNasGX755Ze/ErL/K6Xx48dj/fr1WLVqFVxdXfHyyy8jOjoau3btAqBf3iAqKgre3t7YvXs3MjMz8fzzz8PBwUHJLz09HVFRURg1ahSWLVuGxMREjBgxAj4+PoiMjAQAfPvtt5gwYQIWLVqE0NBQzJkzB5GRkUhLS4Onp6dV9pOIrCM+Hhg7Fjh//q82Pz9g7lwgOtp2eRFRNSD3YPv27TJo0CAJCwuT8+fPi4jIf/7zH9mxY4dF25k2bZq0atWqzL7c3FxxcHCQVatWKW2pqakCQJKSkkREZMOGDWJnZydZWVlKzMKFC8XFxUUKCgpERGTy5MnSrFkzo233799fIiMjlecdOnSQmJgY5blOpxNfX1+JjY01e1/y8vIEgOTl5Zn9GiKyzJo1IiqVCGD8UKn0jzVrbJ0hEVU1lnx/W3x4bs2aNYiMjISTkxMOHjyIgoICAEBeXt49zT6dOnUKvr6+qF+/PgYNGoRz584BAJKTk1FUVISIiAglNiQkBAEBAUhKSgKgv5KvRYsW8PLyUmIiIyORn5+PY8eOKTGlt2GIMWyjsLAQycnJRjF2dnaIiIhQYojI9nQ6/QxTWYukGNrGjeOhOiIqPxYXTe+99x4WLVqEf//733BwcFDaO3XqhIMHD1q0rdDQUMTFxWHjxo1YuHAh0tPTER4ejmvXriErKwsajQZubm5Gr/Hy8kJWVhYAICsry6hgMvQb+u4Wk5+fj5s3b+Ly5cvQ6XRlxhi2UZaCggLk5+cbPYio/OzYYXxI7nYiQEaGPo6IqDxYfE5TWloaunTpYtLu6uqK3Nxci7bVs2dP5e8tW7ZEaGgoAgMDsXLlSjg5OVmaWoWKjY3FjBkzbJ0GUbWRmWndOCIiS1k80+Tt7Y3ffvvNpH3nzp2oX7/+fSXj5uaGxo0b47fffoO3tzcKCwtNCrHs7Gx4e3srudx+NZ3h+d/FuLi4wMnJCe7u7lCr1WXGGLZRlilTpiAvL095ZGRk3NM+E5F5fHysG0dEZCmLi6YXX3wRY8eOxd69e6FSqXDx4kUsW7YMr732GkaPHn1fyVy/fh2nT5+Gj48P2rZtCwcHByQmJir9aWlpOHfunHLPu7CwMBw5cgQ5OTlKTEJCAlxcXNC0aVMlpvQ2DDGGbWg0GrRt29YopqSkBImJiUpMWbRaLVxcXIweRFR+wsP1V8nd6d7hKhXg76+PIyIqF5aeZV5SUiLvvfee1KhRQ1QqlahUKnF0dJS33nrL4jPWJ06cKFu3bpX09HTZtWuXREREiLu7u+Tk5IiIyKhRoyQgIEA2b94sBw4ckLCwMAkLC1NeX1xcLM2bN5fu3btLSkqKbNy4UTw8PGTKlClKzJkzZ8TZ2VkmTZokqampMn/+fFGr1bJx40YlZsWKFaLVaiUuLk6OHz8uI0eOFDc3N6Or8v4Or54jKn+Gq+duv4KOV88R0b2y5Pv7npYcEBEpKCiQY8eOyd69e+XatWv3tI3+/fuLj4+PaDQaqVevnvTv319+++03pf/mzZsyZswYqV27tjg7O0ufPn0kMzPTaBtnz56Vnj17ipOTk7i7u8vEiROlqKjIKGbLli3SunVr0Wg0Ur9+fVmyZIlJLp999pkEBASIRqORDh06yJ49eyzaFxZNRBVjzRoRPz/josnfnwUTEd0bS76/VSJlXcB7Z8OHD8fcuXNRq1Yto/YbN27glVdewVdffWW1WbCqxJK7JBPR/dHp9FfJZWbqz2EKDwfUaltnRURVkSXf3xYXTWq1GpmZmSYrZV++fBne3t4mtzCpLlg0ERERVT2WfH+bveRAfn4+RH84D9euXYOjo6PSp9PpsGHDBt5yhIiIiB5YZhdNbm5uyk11GzdubNKvUqm4bhERERE9sMwumrZs2QIRweOPP441a9agTp06Sp9Go0FgYCB8fX3LJUkiIiIiWzO7aHr00UcBAOnp6QgICIDqToulEBHdhiduE9GDwOLFLTdv3ozVq1ebtK9atQpLly61SlJE9OCIjweCgoDHHgP+7//0fwYF6duJiKoSi4um2NhYuLu7m7R7enri/ffft0pSRPRgiI8H+vUzvdHuhQv6dhZORFSVWFw0nTt3DsHBwSbtgYGBOHfunFWSIqKqT6cDxo7VLz95O0PbuHH6OCKiqsDiosnT0xOHDx82aT906BDq1q1rlaSIqOrbscN0hqk0ESAjQx9HRFQVWFw0DRw4EK+++iq2bNkCnU4HnU6HzZs3Y+zYsRgwYEB55EhEVVBmpnXjiIhszeyr5wzeffddnD17Ft26dYO9vf7lJSUleP7553lOExEpfHysG0dEZGsW30bF4OTJkzh06BCcnJzQokULBAYGWju3KoW3USEyptPpr5K7cKHs85pUKsDPD0hP5/IDRGQ75XIblds1bty4zJXBiYgAfSE0d67+KjmVyrhwMizzNmcOCyYiqjrMKpomTJiAd999FzVq1MCECRPuGvuvf/3LKokRUdUXHQ2sXq2/iq70SeF+fvqCKTraZqkREVnMrKLp119/RVFRkfL3O+Eq4UR0u+hooFcvrghORFXfPZ/TRMZ4ThMREVHVY8n3t8VLDhARERFVR2Ydnou24MSDeN4XgYiIiB5AZs00ubq6Kg8XFxckJibiwIEDSn9ycjISExPh6upabokSERER2ZJZM01LlixR/v7666/j2WefxaJFi6D+35mcOp0OY8aM4bk8RERE9MCy+ERwDw8P7Ny5E02aNDFqT0tLQ8eOHfHHH39YNcGqgieCExERVT3leiJ4cXExTpw4YdJ+4sQJlJSUWLo5IiIioirB4hXBhw0bhhdeeAGnT59Ghw4dAAB79+7FrFmzMGzYMKsnSERERFQZWFw0ffTRR/D29sbHH3+MzP/dntzHxweTJk3CxIkTrZ4gERERUWVwX4tb5ufnAwDP4QHPaSIiIqqKyn1xy+LiYvzyyy9Yvny5cuuUixcv4vr16/eyOSIiIqJKz+LDc7///jt69OiBc+fOoaCgAP/4xz9Qq1YtfPDBBygoKMCiRYvKI08iIiIim7J4pmns2LFo164drl69CicnJ6W9T58+SExMtGpyRERERJWFxTNNO3bswO7du6HRaIzag4KCcOHCBaslRkRERFSZWDzTVFJSAp1OZ9J+/vx51KpVyypJEREREVU2FhdN3bt3x5w5c5TnKpUK169fx7Rp0/DEE09YMzciIiKiSsPiJQcyMjLQo0cPiAhOnTqFdu3a4dSpU3B3d8f27dvh6elZXrlWalxygCoTnQ7YsQPIzAR8fIDwcOB/t4okIqJSLPn+vqd1moqLi/Htt9/i0KFDuH79Otq0aYNBgwYZnRhe3bBoosoiPh4YOxY4f/6vNj8/YO5cIDradnkREVVG5VY0FRUVISQkBOvWrcNDDz1034k+SFg0UWUQHw/06wfc/q/6f8upYfVqFk5ERKWV2+KWDg4OuHXr1n0lR0TlQ6fTzzCV9d8gQ9u4cfo4IiKynMUngsfExOCDDz5AcXFxeeRDRPdoxw7jQ3K3EwEyMvRxRERkOYvXadq/fz8SExPx888/o0WLFqhRo4ZRf3x8vNWSIyLz/e/+2VaLIyIiYxYXTW5ubujbt2955EJE98HHx7pxRERk7J6uniNTPBGcbE2nA4KCgAsXyj6vSaXSX0WXns7lB4iIDMrlRPCSkhJ88MEH6NSpE9q3b4833ngDN2/evO9kicg61Gr9sgLAX1fLGRiez5nDgomI6F6ZXTTNnDkT//znP1GzZk3Uq1cPc+fORUxMTHnmRkQWio7WLytQr55xu58flxsgIrpfZh+ea9SoEV577TW89NJLAIBffvkFUVFRuHnzJuzsLL4I74HDw3NUmXBFcCIi85TL4blz584Z3VsuIiICKpUKFy9evPdMS5k1axZUKhXGjRuntN26dQsxMTGoW7cuatasib59+yI7O9skr6ioKDg7O8PT0xOTJk0yWQ5h69ataNOmDbRaLRo2bIi4uDiT958/fz6CgoLg6OiI0NBQ7Nu3zyr7RWQLajXQtSswcKD+TxZMRET3z+yiqbi4GI6OjkZtDg4OKCoquu8k9u/fj88//xwtW7Y0ah8/fjx+/PFHrFq1Ctu2bcPFixcRXer4gk6nQ1RUFAoLC7F7924sXboUcXFxmDp1qhKTnp6OqKgoPPbYY0hJScG4ceMwYsQIbNq0SYn59ttvMWHCBEybNg0HDx5Eq1atEBkZiZycnPveNyIiInowmH14zs7ODj179oRWq1XafvzxRzz++ONGazVZuk6T4d51CxYswHvvvYfWrVtjzpw5yMvLg4eHB7755hv069cPAHDixAk89NBDSEpKwiOPPIKffvoJTz75JC5evAgvLy8AwKJFi/D666/j0qVL0Gg0eP3117F+/XocPXpUec8BAwYgNzcXGzduBACEhoaiffv2mDdvHgD9Se/+/v545ZVX8MYbb5i1Hzw8R0REVPWUy+G5IUOGwNPTE66ursrjueeeg6+vr1GbpWJiYhAVFYWIiAij9uTkZBQVFRm1h4SEICAgAElJSQCApKQktGjRQimYACAyMhL5+fk4duyYEnP7tiMjI5VtFBYWIjk52SjGzs4OERERSgwRERGR2YtbLlmyxOpvvmLFChw8eBD79+836cvKyoJGo4Gbm5tRu5eXF7KyspSY0gWTod/Qd7eY/Px83Lx5E1evXoVOpysz5sSJE3fMvaCgAAUFBcrz/Pz8v9lbIiIiqspsdtlbRkYGxo4di2XLlpmcK1UVxMbGGs2w+fv72zolIiIiKkc2K5qSk5ORk5ODNm3awN7eHvb29ti2bRs+/fRT2Nvbw8vLC4WFhcjNzTV6XXZ2Nry9vQEA3t7eJlfTGZ7/XYyLiwucnJzg7u4OtVpdZoxhG2WZMmUK8vLylEdGRsY9jQMRERFVDTYrmrp164YjR44gJSVFebRr1w6DBg1S/u7g4IDExETlNWlpaTh37hzCwsIAAGFhYThy5IjRVW4JCQlwcXFB06ZNlZjS2zDEGLah0WjQtm1bo5iSkhIkJiYqMWXRarVwcXExehAREdGDy+Ib9lpLrVq10Lx5c6O2GjVqoG7dukr7Cy+8gAkTJqBOnTpwcXHBK6+8grCwMDzyyCMAgO7du6Np06YYPHgwZs+ejaysLLz11luIiYlRrvIbNWoU5s2bh8mTJ2P48OHYvHkzVq5cifXr1yvvO2HCBAwZMgTt2rVDhw4dMGfOHNy4cQPDhg2roNEgIiKiys5mRZM5PvnkE9jZ2aFv374oKChAZGQkFixYoPSr1WqsW7cOo0ePRlhYGGrUqIEhQ4bgnXfeUWKCg4Oxfv16jB8/HnPnzoWfnx++/PJLREZGKjH9+/fHpUuXMHXqVGRlZaF169bYuHGjycnhREREVH2ZvU4T3R3XaSIiIqp6ymWdJiIiIqLqjEUTERERkRlYNBERERGZgUUTERERkRkq9dVzRFWJTgfs2AFkZgI+PkB4OKBW2zorIiKyFhZNRFYQHw+MHQucP/9Xm58fMHcuEB1tu7yIiMh6eHiO6D7FxwP9+hkXTABw4YK+PT7eNnkREZF1sWgiug86nX6GqazVzgxt48bp44iIqGpj0UR0H3bsMJ1hKk0EyMjQxxERUdXGoonoPmRmWjeOiIgqLxZNRPfBx8e6cUREVHmxaCK6D+Hh+qvkVKqy+1UqwN9fH0dERFUbiyai+6BW65cVAEwLJ8PzOXO4XhMR0YOARRPRfYqOBlavBurVM27389O3c50mIqIHAxe3JLKC6GigVy+uCE5E9CBj0URkJWo10LWrrbMgIqLywsNzRERERGbgTBMReLNdIiL6eyyaqNrjzXaJiMgcPDxH1RpvtktEROZi0UTVFm+2S0RElmDRRNUWb7ZLRESWYNFE1RZvtktERJZg0UTVFm+2S0RElmDRRNUWb7ZLRESWYNFE1RZvtktERJZg0UTVGm+2S0RE5uLilvRAuZeVvXmzXSIiMgeLJnpg3M/K3rzZLhER/R0enqMHAlf2JiKi8saiiao8ruxNREQVgUUTVXlc2ZuIiCoCiyaq8riyNxERVQQWTVTlcWVvIiKqCCyaqMrjyt5ERFQRWDRRlceVvYmIqCKwaKIHAlf2JiKi8sbFLemBwZW9iYioPLFoogcKV/YmIqLywsNzRERERGZg0URERERkBhZNRERERGawadG0cOFCtGzZEi4uLnBxcUFYWBh++uknpf/WrVuIiYlB3bp1UbNmTfTt2xfZ2dlG2zh37hyioqLg7OwMT09PTJo0CcXFxUYxW7duRZs2baDVatGwYUPExcWZ5DJ//nwEBQXB0dERoaGh2LdvX7nsMxEREVVNNi2a/Pz8MGvWLCQnJ+PAgQN4/PHH0atXLxw7dgwAMH78ePz4449YtWoVtm3bhosXLyK61LXjOp0OUVFRKCwsxO7du7F06VLExcVh6tSpSkx6ejqioqLw2GOPISUlBePGjcOIESOwadMmJebbb7/FhAkTMG3aNBw8eBCtWrVCZGQkcnJyKm4wiIiIqHKTSqZ27dry5ZdfSm5urjg4OMiqVauUvtTUVAEgSUlJIiKyYcMGsbOzk6ysLCVm4cKF4uLiIgUFBSIiMnnyZGnWrJnRe/Tv318iIyOV5x06dJCYmBjluU6nE19fX4mNjTU777y8PAEgeXl5lu0wERER2Ywl39+V5pwmnU6HFStW4MaNGwgLC0NycjKKiooQERGhxISEhCAgIABJSUkAgKSkJLRo0QJeXl5KTGRkJPLz85XZqqSkJKNtGGIM2ygsLERycrJRjJ2dHSIiIpQYIiIiIpuv03TkyBGEhYXh1q1bqFmzJr777js0bdoUKSkp0Gg0cHNzM4r38vJCVlYWACArK8uoYDL0G/ruFpOfn4+bN2/i6tWr0Ol0ZcacOHHijnkXFBSgoKBAeZ6fn2/ZjhMREVGVYvOZpiZNmiAlJQV79+7F6NGjMWTIEBw/ftzWaf2t2NhYuLq6Kg9/f39bp0RERETlyOZFk0ajQcOGDdG2bVvExsaiVatWmDt3Lry9vVFYWIjc3Fyj+OzsbHh7ewMAvL29Ta6mMzz/uxgXFxc4OTnB3d0darW6zBjDNsoyZcoU5OXlKY+MjIx72n8iIiKqGmxeNN2upKQEBQUFaNu2LRwcHJCYmKj0paWl4dy5cwgLCwMAhIWF4ciRI0ZXuSUkJMDFxQVNmzZVYkpvwxBj2IZGo0Hbtm2NYkpKSpCYmKjElEWr1SpLJRgeZEynA7ZuBZYv1/+p09k6IyIiovtQASem39Ebb7wh27Ztk/T0dDl8+LC88cYbolKp5OeffxYRkVGjRklAQIBs3rxZDhw4IGFhYRIWFqa8vri4WJo3by7du3eXlJQU2bhxo3h4eMiUKVOUmDNnzoizs7NMmjRJUlNTZf78+aJWq2Xjxo1KzIoVK0Sr1UpcXJwcP35cRo4cKW5ubkZX5f0dXj1nbM0aET8/EeCvh5+fvp2IiKiysOT726ZF0/DhwyUwMFA0Go14eHhIt27dlIJJROTmzZsyZswYqV27tjg7O0ufPn0kMzPTaBtnz56Vnj17ipOTk7i7u8vEiROlqKjIKGbLli3SunVr0Wg0Ur9+fVmyZIlJLp999pkEBASIRqORDh06yJ49eyzaFxZNf1mzRkSlMi6YAH2bSsXCiYiIKg9Lvr9VIiK2net6MOTn58PV1RV5eXnV+lCdTgcEBQHnz5fdr1IBfn5AejqgVldoakRERCYs+f6udOc0UdW2Y8edCyZAP+eUkaGPIyIiqkpYNJFVZWZaN46IiKiyYNFEVuXjY904IiKiyoJFE1lVeLj+nCWVqux+lQrw99fHERERVSUsmsiq1Gpg7lz9328vnAzP58zhSeBERFT1sGgiq4uOBlavBurVM27389O3R0fbJi8iIqL7YfMb9tKDKToa6NVLf5VcZqb+HKbwcM4wERFR1cWiicqNWg107WrrLIiIiKyDh+eIiIiIzMCiiYiIiMgMLJqIiIiIzMCiiYiIiMgMLJqIiIiIzMCiiYiIiMgMLJqIiIiIzMB1mqohnY6LThIREVmKRVM1Ex8PjB0LnD//V5ufn/5+cby9CRER0Z3x8Fw1Eh8P9OtnXDABwIUL+vb4eNvkRUREVBWwaKomdDr9DJOIaZ+hbdw4fRwRERGZYtFUTezYYTrDVJoIkJGhjyMiIiJTLJqqicxM68YRERFVNyyaqgkfH+vGERERVTcsmqqJ8HD9VXIqVdn9KhXg76+PIyIiIlMsmqoJtVq/rABgWjgZns+Zw/WaiIiI7oRFUzUSHQ2sXg3Uq2fc7uenb+c6TURERHfGxS2rmehooFcvrghORERkKRZN1ZBaDXTtaussiIiIqhYeniMiIiIyA4smIiIiIjOwaCIiIiIyA4smIiIiIjPwRPBKTqfjlW5ERESVAYumSiw+Hhg71vhGu35++kUquaYSERFRxeLhuUoqPh7o18+4YAKACxf07fHxtsmLiIioumLRVAnpdPoZJhHTPkPbuHH6OCIiIqoYLJoqoR07TGeYShMBMjL0cURERFQxWDRVQpmZ1o0jIiKi+8eiqRLy8bFuHBEREd0/Fk2VUHi4/io5larsfpUK8PfXxxEREVHFYNFUCanV+mUFANPCyfB8zhyu10RERFSRWDRVUtHRwOrVQL16xu1+fvp2rtNERERUsbi4ZSUWHQ306sUVwYmIiCoDm840xcbGon379qhVqxY8PT3Ru3dvpKWlGcXcunULMTExqFu3LmrWrIm+ffsiOzvbKObcuXOIioqCs7MzPD09MWnSJBQXFxvFbN26FW3atIFWq0XDhg0RFxdnks/8+fMRFBQER0dHhIaGYt++fVbfZ0up1UDXrsDAgfo/WTARERHZhk2Lpm3btiEmJgZ79uxBQkICioqK0L17d9y4cUOJGT9+PH788UesWrUK27Ztw8WLFxFd6tiUTqdDVFQUCgsLsXv3bixduhRxcXGYOnWqEpOeno6oqCg89thjSElJwbhx4zBixAhs2rRJifn2228xYcIETJs2DQcPHkSrVq0QGRmJnJycihkMIiIiqtykEsnJyREAsm3bNhERyc3NFQcHB1m1apUSk5qaKgAkKSlJREQ2bNggdnZ2kpWVpcQsXLhQXFxcpKCgQEREJk+eLM2aNTN6r/79+0tkZKTyvEOHDhITE6M81+l04uvrK7GxsWblnpeXJwAkLy/Pwr0mIiIiW7Hk+7tSnQiel5cHAKhTpw4AIDk5GUVFRYiIiFBiQkJCEBAQgKSkJABAUlISWrRoAS8vLyUmMjIS+fn5OHbsmBJTehuGGMM2CgsLkZycbBRjZ2eHiIgIJYaIiIiqt0pzInhJSQnGjRuHTp06oXnz5gCArKwsaDQauLm5GcV6eXkhKytLiSldMBn6DX13i8nPz8fNmzdx9epV6HS6MmNOnDhRZr4FBQUoKChQnufn51u4x0RERFSVVJqZppiYGBw9ehQrVqywdSpmiY2Nhaurq/Lw9/e3dUpERERUjipF0fTyyy9j3bp12LJlC/z8/JR2b29vFBYWIjc31yg+Ozsb3t7eSsztV9MZnv9djIuLC5ycnODu7g61Wl1mjGEbt5syZQry8vKUR0ZGhuU7TkRERFWGTYsmEcHLL7+M7777Dps3b0ZwcLBRf9u2beHg4IDExESlLS0tDefOnUNYWBgAICwsDEeOHDG6yi0hIQEuLi5o2rSpElN6G4YYwzY0Gg3atm1rFFNSUoLExEQl5nZarRYuLi5GDyIiInqAlf956Xc2evRocXV1la1bt0pmZqby+PPPP5WYUaNGSUBAgGzevFkOHDggYWFhEhYWpvQXFxdL8+bNpXv37pKSkiIbN24UDw8PmTJlihJz5swZcXZ2lkmTJklqaqrMnz9f1Gq1bNy4UYlZsWKFaLVaiYuLk+PHj8vIkSPFzc3N6Kq8u+HVc0RERFWPJd/fNi2aAJT5WLJkiRJz8+ZNGTNmjNSuXVucnZ2lT58+kpmZabSds2fPSs+ePcXJyUnc3d1l4sSJUlRUZBSzZcsWad26tWg0Gqlfv77Rexh89tlnEhAQIBqNRjp06CB79uwxe19YNBEREVU9lnx/q0REbDXL9SDJy8uDm5sbMjIyeKiOiIioisjPz4e/vz9yc3Ph6up619hKs+RAVXft2jUA4FV0REREVdC1a9f+tmjiTJOVlJSU4OLFi6hVqxZUKpVJv6GS5UyUKY7N3XF87oxjc2ccmzvj2NxddRsfEcG1a9fg6+sLO7u7Xx/HmSYrsbOzM1ou4U54pd2dcWzujuNzZxybO+PY3BnH5u6q0/j83QyTQaVYp4mIiIiosmPRRERERGQGFk0VRKvVYtq0adBqtbZOpdLh2Nwdx+fOODZ3xrG5M47N3XF87ownghMRERGZgTNNRERERGZg0URERERkBhZNRERERGZg0URERERkBhZNdxAbG4v27dujVq1a8PT0RO/evZGWlmYUc+vWLcTExKBu3bqoWbMm+vbti+zsbKOYc+fOISoqCs7OzvD09MSkSZNQXFxsFLN161a0adMGWq0WDRs2RFxcnEk+8+fPR1BQEBwdHREaGop9+/ZZfZ8tsXDhQrRs2VJZ/CwsLAw//fST0l+dx6a0WbNmQaVSYdy4cUpbdR6b6dOnQ6VSGT1CQkKU/uo8NgBw4cIFPPfcc6hbty6cnJzQokULHDhwQOkXEUydOhU+Pj5wcnJCREQETp06ZbSNK1euYNCgQXBxcYGbmxteeOEFXL9+3Sjm8OHDCA8Ph6OjI/z9/TF79myTXFatWoWQkBA4OjqiRYsW2LBhQ/nstJmCgoJMPjsqlQoxMTEAqvdnR6fT4e2330ZwcDCcnJzQoEEDvPvuuyh9nVd1/uxYVbndNriKi4yMlCVLlsjRo0clJSVFnnjiCQkICJDr168rMaNGjRJ/f39JTEyUAwcOyCOPPCIdO3ZU+ouLi6V58+YSEREhv/76q2zYsEHc3d1lypQpSsyZM2fE2dlZJkyYIMePH5fPPvtM1Gq1bNy4UYlZsWKFaDQa+eqrr+TYsWPy4osvipubm2RnZ1fMYJThhx9+kPXr18vJkyclLS1N/vnPf4qDg4McPXpURKr32Bjs27dPgoKCpGXLljJ27FilvTqPzbRp06RZs2aSmZmpPC5duqT0V+exuXLligQGBsrQoUNl7969cubMGdm0aZP89ttvSsysWbPE1dVV1q5dK4cOHZKnn35agoOD5ebNm0pMjx49pFWrVrJnzx7ZsWOHNGzYUAYOHKj05+XliZeXlwwaNEiOHj0qy5cvFycnJ/n888+VmF27dolarZbZs2fL8ePH5a233hIHBwc5cuRIxQxGGXJycow+NwkJCQJAtmzZIiLV+7Mzc+ZMqVu3rqxbt07S09Nl1apVUrNmTZk7d64SU50/O9bEoslMOTk5AkC2bdsmIiK5ubni4OAgq1atUmJSU1MFgCQlJYmIyIYNG8TOzk6ysrKUmIULF4qLi4sUFBSIiMjkyZOlWbNmRu/Vv39/iYyMVJ536NBBYmJilOc6nU58fX0lNjbW+jt6H2rXri1ffvklx0ZErl27Jo0aNZKEhAR59NFHlaKpuo/NtGnTpFWrVmX2Vfexef3116Vz58537C8pKRFvb2/58MMPlbbc3FzRarWyfPlyERE5fvy4AJD9+/crMT/99JOoVCq5cOGCiIgsWLBAateurYyX4b2bNGmiPH/22WclKirK6P1DQ0PlpZdeur+dtKKxY8dKgwYNpKSkpNp/dqKiomT48OFGbdHR0TJo0CAR4WfHmnh4zkx5eXkAgDp16gAAkpOTUVRUhIiICCUmJCQEAQEBSEpKAgAkJSWhRYsW8PLyUmIiIyORn5+PY8eOKTGlt2GIMWyjsLAQycnJRjF2dnaIiIhQYmxNp9NhxYoVuHHjBsLCwjg2AGJiYhAVFWWSP8cGOHXqFHx9fVG/fn0MGjQI586dA8Cx+eGHH9CuXTs888wz8PT0xMMPP4x///vfSn96ejqysrKM8nZ1dUVoaKjR+Li5uaFdu3ZKTEREBOzs7LB3714lpkuXLtBoNEpMZGQk0tLScPXqVSXmbmNoa4WFhfj6668xfPhwqFSqav/Z6dixIxITE3Hy5EkAwKFDh7Bz50707NkTAD871sSiyQwlJSUYN24cOnXqhObNmwMAsrKyoNFo4ObmZhTr5eWFrKwsJab0P1BDv6HvbjH5+fm4efMmLl++DJ1OV2aMYRu2cuTIEdSsWRNarRajRo3Cd999h6ZNm1b7sVmxYgUOHjyI2NhYk77qPjahoaGIi4vDxo0bsXDhQqSnpyM8PBzXrl2r9mNz5swZLFy4EI0aNcKmTZswevRovPrqq1i6dCmAv/bvbnlnZWXB09PTqN/e3h516tSxyhja+neOwdq1a5Gbm4uhQ4cC4L+rN954AwMGDEBISAgcHBzw8MMPY9y4cRg0aBAAfnasyd7WCVQFMTExOHr0KHbu3GnrVCqVJk2aICUlBXl5eVi9ejWGDBmCbdu22Totm8rIyMDYsWORkJAAR0dHW6dT6Rj+5wsALVu2RGhoKAIDA7Fy5Uo4OTnZMDPbKykpQbt27fD+++8DAB5++GEcPXoUixYtwpAhQ2ycXeWyePFi9OzZE76+vrZOpVJYuXIlli1bhm+++QbNmjVDSkoKxo0bB19fX352rIwzTX/j5Zdfxrp167Blyxb4+fkp7d7e3igsLERubq5RfHZ2Nry9vZWY26/eMDz/uxgXFxc4OTnB3d0darW6zBjDNmxFo9GgYcOGaNu2LWJjY9GqVSvMnTu3Wo9NcnIycnJy0KZNG9jb28Pe3h7btm3Dp59+Cnt7e3h5eVXbsSmLm5sbGjdujN9++61af24AwMfHB02bNjVqe+ihh5TDl4bc7pa3t7c3cnJyjPqLi4tx5coVq4xhZfjs/P777/jll18wYsQIpa26f3YmTZqkzDa1aNECgwcPxvjx45XZbn52rIdF0x2ICF5++WV899132Lx5M4KDg43627ZtCwcHByQmJiptaWlpOHfuHMLCwgAAYWFhOHLkiNEHMSEhAS4uLsovx7CwMKNtGGIM29BoNGjbtq1RTElJCRITE5WYyqKkpAQFBQXVemy6deuGI0eOICUlRXm0a9cOgwYNUv5eXcemLNevX8fp06fh4+NTrT83ANCpUyeTZU1OnjyJwMBAAEBwcDC8vb2N8s7Pz8fevXuNxic3NxfJyclKzObNm1FSUoLQ0FAlZvv27SgqKlJiEhIS0KRJE9SuXVuJudsY2tKSJUvg6emJqKgopa26f3b+/PNP2NkZf52r1WqUlJQA4GfHqmx9JnplNXr0aHF1dZWtW7caXeb6559/KjGjRo2SgIAA2bx5sxw4cEDCwsIkLCxM6Tdc4tq9e3dJSUmRjRs3ioeHR5mXuE6aNElSU1Nl/vz5ZV7iqtVqJS4uTo4fPy4jR44UNzc3o6tAKtobb7wh27Ztk/T0dDl8+LC88cYbolKp5OeffxaR6j02tyt99ZxI9R6biRMnytatWyU9PV127dolERER4u7uLjk5OSJSvcdm3759Ym9vLzNnzpRTp07JsmXLxNnZWb7++mslZtasWeLm5ibff/+9HD58WHr16lXmZeMPP/yw7N27V3bu3CmNGjUyumw8NzdXvLy8ZPDgwXL06FFZsWKFODs7m1w2bm9vLx999JGkpqbKtGnTKsVl4zqdTgICAuT111836avOn50hQ4ZIvXr1lCUH4uPjxd3dXSZPnqzEVPfPjrWwaLoDAGU+lixZosTcvHlTxowZI7Vr1xZnZ2fp06ePZGZmGm3n7Nmz0rNnT3FychJ3d3eZOHGiFBUVGcVs2bJFWrduLRqNRurXr2/0HgafffaZBAQEiEajkQ4dOsiePXvKY7fNNnz4cAkMDBSNRiMeHh7SrVs3pWASqd5jc7vbi6bqPDb9+/cXHx8f0Wg0Uq9ePenfv7/ROkTVeWxERH788Udp3ry5aLVaCQkJkS+++MKov6SkRN5++23x8vISrVYr3bp1k7S0NKOYP/74QwYOHCg1a9YUFxcXGTZsmFy7ds0o5tChQ9K5c2fRarVSr149mTVrlkkuK1eulMaNG4tGo5FmzZrJ+vXrrb/DFtq0aZMAMNlnker92cnPz5exY8dKQECAODo6Sv369eXNN980Whqgun92rEUlUmrJUCIiIiIqE89pIiIiIjIDiyYiIiIiM7BoIiIiIjIDiyYiIiIiM7BoIiIiIjIDiyYiIiIiM7BoIiIiIjIDiyYieiCpVCqsXbvW1mkAAIYOHYrevXuX+/sEBQVhzpw55f4+RNUViyYiui9JSUlQq9VG9wIzly2/5IcOHQqVSgWVSqXcfPqdd95BcXGxTfKxRFxcHNzc3Eza9+/fj5EjR1Z8QkTVBIsmIrovixcvxiuvvILt27fj4sWLtk7HIj169EBmZiZOnTqFiRMnYvr06fjwww/LjC0sLKzg7Czn4eEBZ2dnW6dB9MBi0URE9+z69ev49ttvMXr0aERFRSEuLs4k5scff0T79u3h6OgId3d39OnTBwDQtWtX/P777xg/frwy4wMA06dPR+vWrY22MWfOHAQFBSnP9+/fj3/84x9wd3eHq6srHn30URw8eNDi/LVaLby9vREYGIjRo0cjIiICP/zwA4C/DqnNnDkTvr6+aNKkCQDgyJEjePzxx+Hk5IS6deti5MiRuH79urJNnU6HCRMmwM3NDXXr1sXkyZNx+92qyppha926NaZPn648z83NxUsvvQQvLy84OjqiefPmWLduHbZu3Yphw4YhLy9PGTfD627f7rlz59CrVy/UrFkTLi4uePbZZ5Gdna30G8b6v//9L4KCguDq6ooBAwbg2rVrFo8lUXXAoomI7tnKlSsREhKCJk2a4LnnnsNXX31lVCCsX78effr0wRNPPIFff/0ViYmJ6NChAwAgPj4efn5+eOedd5CZmYnMzEyz3/fatWsYMmQIdu7ciT179qBRo0Z44okn7vvL3snJyWhGKTExEWlpaUhISMC6detw48YNREZGonbt2ti/fz9WrVqFX375BS+//LLymo8//hhxcXH46quvsHPnTly5cgXfffedRXmUlJSgZ8+e2LVrF77++mscP34cs2bNglqtRseOHTFnzhy4uLgo4/baa6+VuY1evXrhypUr2LZtGxISEnDmzBn079/fKO706dNYu3Yt1q1bh3Xr1mHbtm2YNWuWhSNHVD3Y2zoBIqq6Fi9ejOeeew6A/lBXXl4etm3bhq5duwIAZs6ciQEDBmDGjBnKa1q1agUAqFOnDtRqNWrVqgVvb2+L3vfxxx83ev7FF1/Azc0N27Ztw5NPPmnxfogIEhMTsWnTJrzyyitKe40aNfDll19Co9EAAP7973/j1q1b+M9//oMaNWoAAObNm4ennnoKH3zwAby8vDBnzhxMmTIF0dHRAIBFixZh06ZNFuXzyy+/YN++fUhNTUXjxo0BAPXr11f6XV1doVKp7jpuiYmJOHLkCNLT0+Hv7w8A+M9//oNmzZph//79aN++PQB9cRUXF4datWoBAAYPHozExETMnDnTopyJqgPONBHRPUlLS8O+ffswcOBAAIC9vT369++PxYsXKzEpKSno1q2b1d87OzsbL774Iho1agRXV1e4uLjg+vXrOHfunEXbWbduHWrWrAlHR0f07NkT/fv3NzpE1qJFC6VgAoDU1FS0atVKKZgAoFOnTigpKUFaWhry8vKQmZmJ0NBQpd/e3h7t2rWzKK+UlBT4+fkpBdO9SE1Nhb+/v1IwAUDTpk3h5uaG1NRUpS0oKEgpmADAx8cHOTk59/y+RA8yzjQR0T1ZvHgxiouL4evrq7SJCLRaLebNmwdXV1c4OTlZvF07OzuTc4CKioqMng8ZMgR//PEH5s6di8DAQGi1WoSFhVl8svZjjz2GhQsXQqPRwNfXF/b2xr8SSxdH1vR3+3gv43avHBwcjJ6rVCqUlJRU2PsTVSWcaSIiixUXF+M///kPPv74Y6SkpCiPQ4cOwdfXF8uXLwcAtGzZEomJiXfcjkajgU6nM2rz8PBAVlaWUVGRkpJiFLNr1y68+uqreOKJJ9CsWTNotVpcvnzZ4v2oUaMGGjZsiICAAJOCqSwPPfQQDh06hBs3bhjlYmdnhyZNmsDV1RU+Pj7Yu3ev0l9cXIzk5GSTfSx9Dld+fj7S09OV5y1btsT58+dx8uTJMvMoa9zKyjUjIwMZGRlK2/Hjx5Gbm4umTZv+7b4SkSkWTURksXXr1uHq1at44YUX0Lx5c6NH3759lUN006ZNw/LlyzFt2jSkpqbiyJEj+OCDD5TtBAUFYfv27bhw4YJS9HTt2hWXLl3C7Nmzcfr0acyfPx8//fST0fs3atQI//3vf5Gamoq9e/di0KBBFTI7M2jQIDg6OmLIkCE4evQotmzZgldeeQWDBw+Gl5cXAGDs2LGYNWsW1q5dixMnTmDMmDHIzc012s7jjz+O//73v9ixYweOHDmCIUOGQK1WK/2PPvoounTpgr59+yIhIQHp6en46aefsHHjRgD6cbt+/ToSExNx+fJl/Pnnnya5RkREoEWLFhg0aBAOHjyIffv24fnnn8ejjz5q8eFCItJj0UREFlu8eDEiIiLg6upq0te3b18cOHAAhw8fRteuXbFq1Sr88MMPaN26NR5//HHs27dPiX3nnXdw9uxZNGjQAB4eHgD0MyQLFizA/Pnz0apVK+zbt8/k6rDFixfj6tWraNOmDQYPHoxXX30Vnp6e5bvTAJydnbFp0yZcuXIF7du3R79+/dCtWzfMmzdPiZk4cSIGDx6MIUOGICwsDLVq1VKWWTCYMmUKHn30UTz55JOIiopC79690aBBA6OYNWvWoH379hg4cCCaNm2KyZMnK7NLHTt2xKhRo9C/f394eHhg9uzZJrmqVCp8//33qF27Nrp06YKIiAjUr18f3377bTmMDFH1oJLbD6wTERERkQnONBERERGZgUUTERERkRlYNBERERGZgUUTERERkRlYNBERERGZgUUTERERkRlYNBERERGZgUUTERERkRlYNBERERGZgUUTERERkRlYNBERERGZgUUTERERkRn+H/+GyOMoN+SnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 根据提供的数据创建一个字典\n",
    "data = {'年份': [2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021],\n",
    "    '电力可供量(亿千瓦小时)': [21972.3, 24940.8, 28588.4, 32712.4, 34540.8, 37032.7, 41936.5, 47002.7, 49767.7, 54204.1, 57830.5, 61204.4, 61504.0, 65914.0, 71509.2, 74866.3, 77620.2, 85200.1],\n",
    "   '电力生产量(亿千瓦小时)':[85342.5, 77790.6, 75034.3, 71661.3,\t66044.5, 61331.6, 58145.7, 57944.6,\t54316.4, 49875.5, 47130.2, 42071.6,\t37146.5, 34668.8, 32815.5, 28657.3,\t25002.6, 22033.1],\n",
    " '电力能源消费总量(亿千瓦小时)':[85200.1,\t77620.2,\t74866.1,\t71508.2,\t65914,\t61205.1,\t58020,\t57829.7,\t54203.4\t,49762.6,\t47000.9,\t41934.5\t,37032.2\t,34541.4\t,32711.8,\t28588,\t24940.3\t,21971.4],\n",
    " '工业电力消费总量(亿千瓦小时)':[56622.3\t,52353.4\t,50698.3\t,49094.9\t,46052.8\t,42996.9,\t41550\t,42248.7\t,39236.9\t,36232.2\t,34691.6,\t30871.8\t,26854.5\t,25388.6\t,24290.8,\t21267.7,\t18521.7\t,16424.3],\n",
    "    '建筑业电力消费总量(亿千瓦小时)':[1132.9\t,1011.1,\t991.2,\t887.8,\t789.2,\t725.6,\t698.7,\t721.7,\t675.1,\t608.4,\t571.8,\t483.2,\t421.9,\t367.3,\t309\t,271\t,233.9,\t202.1],\n",
    "    '居民生活电力消费总量(亿千瓦小时)':[81944,\t74386.7,\t71536\t,68156.5\t,62718.1\t,58142.2,\t55032.1\t,54729.8\t,51062.7\t,46866.5\t,44300.2,\t39366.3,\t34773.9,\t32403.5\t,30650.1\t,26729.1\t,23233.8\t,20550.8],\n",
    "    '电力终端消费量(亿千瓦小时)':[7706.8,\t6517\t,6263.8,\t5716.5,\t4880.6,\t4394.8,\t3918.6,\t3615,\t3397.6,\t3083.6,\t2753.1,\t2451.8,\t2189.9,\t1913,\t1708.6,\t1555.9,\t1340.9,\t1036.6],\n",
    "    '工业终端电力消费量(亿千瓦小时)':[53366.2,\t49120\t,47368.2\t,45743.2,\t42857,\t39934,\t38562.1\t,39148.8\t,36096.2,\t33336.1,\t31990.9\t,28303.5 ,24596.3,23250.8,22229.1,19408.9,16815.2,\t15003.7]\n",
    "}\n",
    "\n",
    "# 创建DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 选择特征和目标变量\n",
    "X = df[['电力能源消费总量(亿千瓦小时)', '电力可供量(亿千瓦小时)', '电力终端消费量(亿千瓦小时)', \n",
    "        '工业终端电力消费量(亿千瓦小时)', '工业电力消费总量(亿千瓦小时)', \n",
    "        '建筑业电力消费总量(亿千瓦小时)', '居民生活电力消费总量(亿千瓦小时)']]\n",
    "y = df['电力生产量(亿千瓦小时)']\n",
    "\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "\n",
    "# 拟合模型\n",
    "model.fit(X, y)\n",
    "\n",
    "# 预测\n",
    "y_pred = model.predict(X)\n",
    "\n",
    "# 评估模型\n",
    "mse = mean_squared_error(y, y_pred)\n",
    "r2 = r2_score(y, y_pred)\n",
    "\n",
    "# 打印评估结果\n",
    "print(f'Mean Squared Error (MSE): {mse}')\n",
    "print(f'R^2 Score: {r2}')\n",
    "\n",
    "# 可视化实际值与预测值\n",
    "plt.scatter(y, y_pred, color='blue')\n",
    "plt.xlabel('Actual Production')\n",
    "plt.ylabel('Predicted Production')\n",
    "plt.title('Actual vs Predicted Electricity Production')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b29a895-ebbc-41ba-9d8d-6f3f0a415ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d08afc4-0edd-485d-99a2-31c15b9b618b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date  电力可供量(亿千瓦小时)  电力生产量(亿千瓦小时)  水电生产电力量(亿千瓦小时)  火电生产电力量(亿千瓦小时)  \\\n",
      "0  2004-01-01       21972.3       22033.1          3535.4         17955.9   \n",
      "1  2005-01-01       24940.8       25002.6          3970.2         20473.4   \n",
      "2  2006-01-01       28588.4       28657.3          4357.9         23696.0   \n",
      "3  2007-01-01       32712.4       32815.5          4852.6         27229.3   \n",
      "4  2008-01-01       34540.8       34668.8          5851.9         27900.8   \n",
      "\n",
      "   核电生产电力量(亿千瓦小时)  风电生产电力量(亿千瓦小时)  电力进口量(亿千瓦小时)  电力出口量(亿千瓦小时)  \\\n",
      "0           504.7             NaN          34.0          94.8   \n",
      "1           530.9             NaN          50.1         111.9   \n",
      "2           548.4             NaN          53.9         122.7   \n",
      "3           621.3             NaN          42.5         145.7   \n",
      "4           683.9             NaN          38.4         166.4   \n",
      "\n",
      "   电力能源消费总量(亿千瓦小时)  农、林、牧、渔业电力消费总量(亿千瓦小时)  工业电力消费总量(亿千瓦小时)  建筑业电力消费总量(亿千瓦小时)  \\\n",
      "0          21971.4                  768.9          16424.3             202.1   \n",
      "1          24940.3                  776.3          18521.7             233.9   \n",
      "2          28588.0                  827.0          21267.7             271.0   \n",
      "3          32711.8                  879.0          24290.8             309.0   \n",
      "4          34541.4                  887.1          25388.6             367.3   \n",
      "\n",
      "   交通运输、仓储和邮政业电力消费总量(亿千瓦小时)  批发和零售业、住宿和餐饮业电力消费总量(亿千瓦小时)  其他电力消费总量(亿千瓦小时)  \\\n",
      "0                     449.6                       705.4           1036.6   \n",
      "1                     430.3                       752.3           1340.9   \n",
      "2                     467.4                       847.3           1555.9   \n",
      "3                     531.9                       929.8           1708.6   \n",
      "4                     571.8                      1017.4           1913.0   \n",
      "\n",
      "   居民生活电力消费总量(亿千瓦小时)  电力终端消费量(亿千瓦小时)  工业终端电力消费量(亿千瓦小时)  输配电损失量(亿千瓦小时)  \n",
      "0             2384.5         20550.8           15003.7         1420.6  \n",
      "1             2884.8         23233.8           16815.2         1706.5  \n",
      "2             3351.6         26729.1           19408.9         1858.8  \n",
      "3             4062.7         30650.1           22229.1         2061.7  \n",
      "4             4396.1         32403.5           23250.8         2137.9  \n",
      "DatetimeIndex(['2004-01-01', '2005-01-01', '2006-01-01', '2007-01-01',\n",
      "               '2008-01-01', '2009-01-01', '2010-01-01', '2011-01-01',\n",
      "               '2012-01-01', '2013-01-01', '2014-01-01', '2015-01-01',\n",
      "               '2016-01-01', '2017-01-01', '2018-01-01', '2019-01-01',\n",
      "               '2020-01-01', '2021-01-01'],\n",
      "              dtype='datetime64[ns]', freq=None)\n",
      "y = 0.0001133838033819529x + -100568.20578319194\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           电力生产量(亿千瓦小时)   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 1.327e+04\n",
      "Date:                Sun, 14 Apr 2024   Prob (F-statistic):           2.08e-24\n",
      "Time:                        20:51:48   Log-Likelihood:                -130.93\n",
      "No. Observations:                  18   AIC:                             269.9\n",
      "Df Residuals:                      14   BIC:                             273.4\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const      -1923.9686    502.587     -3.828      0.002   -3001.911    -846.026\n",
      "x1             1.1093      0.037     29.702      0.000       1.029       1.189\n",
      "x2             0.5590      0.130      4.315      0.001       0.281       0.837\n",
      "x3             3.5583      0.184     19.331      0.000       3.163       3.953\n",
      "==============================================================================\n",
      "Omnibus:                        1.746   Durbin-Watson:                   1.464\n",
      "Prob(Omnibus):                  0.418   Jarque-Bera (JB):                0.624\n",
      "Skew:                           0.433   Prob(JB):                        0.732\n",
      "Kurtosis:                       3.287   Cond. No.                     2.23e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.23e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\Lib\\site-packages\\scipy\\stats\\_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a4b4009190>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 引入必要的库包\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "os.chdir(r'C:\\Users\\A\\Desktop')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('Electricity_20_ffill_date.csv', encoding='utf-8')\n",
    "print(data.head())\n",
    "# 选取数据中的时间和电力生产量两列\n",
    "time = data['date']\n",
    "power = data['电力生产量(亿千瓦小时)']\n",
    "\n",
    "# 用线性拟合的库包进行拟合\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# 将时间转换为时间戳\n",
    "time = pd.to_datetime(time)\n",
    "time = (time - pd.to_datetime('1970-01-01')).dt.total_seconds().values\n",
    "time = time.reshape(-1, 1)\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "# 拟合数据\n",
    "model.fit(time, power)\n",
    "# 预测数据\n",
    "power_predict = model.predict(time)\n",
    "# 将时间变回原来的格式\n",
    "time = pd.to_datetime(time[:, 0], unit='s')\n",
    "print(time)\n",
    "# 给出拟合的函数\n",
    "print('y = {}x + {}'.format(model.coef_[0], model.intercept_))\n",
    "# 计算拟合的误差\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mae =  mean_squared_error(power, power_predict)\n",
    "plt.figure(figsize=(20, 8))\n",
    "# 画出拟合的图像并显示误差\n",
    "plt.plot(time, power, label='True')\n",
    "plt.plot(time, power_predict, label='Predict')\n",
    "plt.title('Linear Regression\\nMAE:{}'.format(mae))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Power')\n",
    "# 设置图像大小\n",
    "plt.legend()\n",
    "plt.show()\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# 选取数据中的特征变量和目标变量\n",
    "target = data[\"电力生产量(亿千瓦小时)\"]\n",
    "features = data[[\"火电生产电力量(亿千瓦小时)\",\"水电生产电力量(亿千瓦小时)\",\"核电生产电力量(亿千瓦小时)\"]]\n",
    "\n",
    "# 将特征变量转换为numpy数组\n",
    "X = np.array(features)\n",
    "\n",
    "# 添加一个常数项，对应截距项\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# 创建线性回归模型\n",
    "model = sm.OLS(target, X).fit()\n",
    "\n",
    "# 打印出回归结果报告\n",
    "print(model.summary())\n",
    "# 用model.predict()函数进行预测\n",
    "predict = model.predict(X)\n",
    "\n",
    "# 计算预测值和真实值之间的均方误差\n",
    "mae = mean_squared_error(target, predict)\n",
    "plt.figure(figsize=(20, 8))\n",
    "# 画出真实值和预测值的对比图\n",
    "plt.plot(time, target, label='True')\n",
    "plt.plot(time, predict, label='Predict')\n",
    "plt.title('Multiple Linear Regression\\nMAE:{}'.format(mae))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Power')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5852b992-e83f-483f-ad9f-721f600af485",
   "metadata": {},
   "outputs": [],
   "source": [
    "#石油"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "070d15a6-023c-4aae-b1b2-615483478730",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      年份  石油可供量(万吨)  石油生产量(万吨)  进口石油量(万吨)  出口石油量(-)(万吨)  年初年末石油库存差额(万吨)  \\\n",
      "0   2021    69048.0    19888.1    58820.0        7616.8         -2043.2   \n",
      "1   2020    67553.7    19476.9    61271.7        7551.0         -5643.8   \n",
      "2   2019    66900.9    19101.4    58102.2        8211.4         -2091.4   \n",
      "3   2018    63726.6    18932.4    54094.3        7557.4         -1742.7   \n",
      "4   2017    60810.8    19150.6    49141.2        7026.7          -454.3   \n",
      "5   2016    57710.6    19968.5    44502.9        6382.9          -377.8   \n",
      "6   2015    55688.0    21455.6    39748.6        5128.2          -388.1   \n",
      "7   2014    52061.8    21142.9    36179.6        4213.9         -1046.8   \n",
      "8   2013    49993.9    20991.9    34264.8        4176.7         -1086.1   \n",
      "9   2012    47864.7    20747.8    33088.8        3884.3         -2087.6   \n",
      "10  2011    45659.2    20287.6    31593.7        4117.0         -2105.0   \n",
      "11  2010    44178.4    20301.4    29437.2        4079.0         -1481.2   \n",
      "12  2009    38692.8    18949.0    25642.4        3916.6         -1981.9   \n",
      "13  2008    37318.8    19044.0    23015.5        2945.7         -1795.0   \n",
      "14  2007    36648.9    18631.8    21139.4        2664.3          -458.0   \n",
      "15  2006    34930.0    18476.6    19453.0        2626.2          -373.3   \n",
      "16  2005    32539.1    18135.3    17163.2        2888.1           128.8   \n",
      "17  2004    32115.5    17587.3    17291.3        2240.6          -522.6   \n",
      "\n",
      "    石油能源消费总量(万吨)  农、林、牧、渔业石油消费总量(万吨)  工业石油消费总量(万吨)  建筑业石油消费总量(万吨)  ...  \\\n",
      "0        68393.4              1981.0       28218.9         4270.1  ...   \n",
      "1        65369.1              1773.1       27711.1         4180.3  ...   \n",
      "2        64506.5              1748.2       25210.6         4055.1  ...   \n",
      "3        62245.1              1724.9       22460.3         3935.7  ...   \n",
      "4        60395.9              1786.4       21486.7         3803.5  ...   \n",
      "5        57692.9              1730.3       20382.5         3599.1  ...   \n",
      "6        55960.2              1733.4       19718.0         3384.3  ...   \n",
      "7        51859.4              1717.7       18357.5         3205.3  ...   \n",
      "8        49970.6              1650.3       17594.6         3090.6  ...   \n",
      "9        47797.3              1537.9       17753.2         2740.7  ...   \n",
      "10       45619.5              1466.3       17986.0         2581.8  ...   \n",
      "11       44101.0              1382.5       18555.0         2483.1  ...   \n",
      "12       38671.4              1308.1       15767.7         2042.3  ...   \n",
      "13       37332.9              1265.8       15285.2         1517.5  ...   \n",
      "14       36654.5              1399.9       14900.8         1823.1  ...   \n",
      "15       34930.8              1540.2       14648.9         1648.5  ...   \n",
      "16       32547.0              1451.7       14030.4         1502.2  ...   \n",
      "17       32072.9              1231.4       14829.1         1392.3  ...   \n",
      "\n",
      "    居民生活石油消费总量(万吨)  石油终端消费量(万吨)  工业终端石油消费量(万吨)  中间消费(用于加工转换)(万吨)  \\\n",
      "0           7504.6      65630.4        25456.9            2743.0   \n",
      "1           7170.1      62085.2        24428.4            3265.6   \n",
      "2           7314.0      61018.5        21732.4            3453.6   \n",
      "3           7328.4      58623.0        18847.6            3593.3   \n",
      "4           7139.7      56880.0        17980.5            3468.9   \n",
      "5           6712.8      54387.0        17100.2            3264.3   \n",
      "6           6162.2      52945.7        16739.7            2926.9   \n",
      "7           5305.2      49309.0        15854.5            2440.0   \n",
      "8           4752.4      47458.8        15235.4            2295.7   \n",
      "9           4291.6      45080.7        15160.4            2534.6   \n",
      "10          3983.9      43103.3        15579.9            2334.5   \n",
      "11          3541.9      41243.4        15857.8            2663.3   \n",
      "12          3167.9      36127.0        13378.7            2355.7   \n",
      "13          2916.9      34510.9        12630.8            2619.7   \n",
      "14          2981.1      34012.6        12424.2            2441.9   \n",
      "15          2609.2      32094.1        11977.4            2636.8   \n",
      "16          2284.4      29495.6        11107.5            2896.0   \n",
      "17          2208.0      28624.7        11505.6            3298.5   \n",
      "\n",
      "    火力发电中间消费石油(万吨)  供热中间消费石油(万吨)  制气中间消费石油(万吨)  炼油损失量(万吨)  石油损失量(万吨)  \\\n",
      "0            324.2         677.6          60.5     1680.7       20.0   \n",
      "1            321.5         677.7          28.9     2232.9       18.3   \n",
      "2            308.3         653.8           3.7     2487.9       34.5   \n",
      "3            309.3         590.2           4.7     2689.0       28.8   \n",
      "4            280.6         522.6           4.7     2661.0       47.0   \n",
      "5            284.6         517.8           4.8     2457.2       41.6   \n",
      "6            265.5         493.2           0.3     2168.2       87.6   \n",
      "7            254.1         521.3           0.3     1664.6      110.3   \n",
      "8            265.1         448.2           0.3     1582.4      216.1   \n",
      "9            292.4         493.5           0.3     1748.7      182.0   \n",
      "10           319.8         525.7           0.3     1489.1      181.7   \n",
      "11           385.3         593.1           0.3     1684.8      194.4   \n",
      "12           393.2         396.2           0.3     1566.1      188.7   \n",
      "13           504.1         418.2           1.6     1695.8      202.4   \n",
      "14           718.6         436.3           5.0     1282.1      200.0   \n",
      "15           931.3         430.9          13.4     1261.3      200.0   \n",
      "16          1306.4         429.1          14.4     1146.1      155.4   \n",
      "17          1653.5         439.4          10.5     1195.1      149.7   \n",
      "\n",
      "    石油平衡差额(万吨)  \n",
      "0        654.6  \n",
      "1       2184.6  \n",
      "2       2394.3  \n",
      "3       1481.5  \n",
      "4        414.9  \n",
      "5         17.7  \n",
      "6       -272.2  \n",
      "7        202.4  \n",
      "8         23.3  \n",
      "9         67.4  \n",
      "10        39.7  \n",
      "11        77.4  \n",
      "12        21.4  \n",
      "13       -14.1  \n",
      "14        -5.6  \n",
      "15        -0.8  \n",
      "16        -7.9  \n",
      "17        42.6  \n",
      "\n",
      "[18 rows x 23 columns]\n",
      "y = 90137770897.83318x + -161831.75837633375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "D:\\anaconda\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.sans-serif\"]=[\"SimHei\"]\n",
    "os.chdir(r'C:\\Users\\A\\Desktop')\n",
    "data = pd.read_csv(\"Petroleum_20_bfill.csv\")\n",
    "print(data.head(18))\n",
    "# 引入必要的库包\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "# 选取数据中的时间和电力生产量两列\n",
    "time = data['年份']\n",
    "power = data['石油生产量(万吨)']\n",
    "\n",
    "# 用线性拟合的库包进行拟合\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# 将时间转换为时间戳\n",
    "time = pd.to_datetime(time)\n",
    "time = (time - pd.to_datetime('1970-01-01')).dt.total_seconds().values\n",
    "time = time.reshape(-1, 1)\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "# 拟合数据\n",
    "model.fit(time, power)\n",
    "# 预测数据\n",
    "power_predict = model.predict(time)\n",
    "# 给出拟合的函数\n",
    "print('y = {}x + {}'.format(model.coef_[0], model.intercept_))\n",
    "# 计算拟合的误差\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mae =  mean_squared_error(power, power_predict)\n",
    "# 绘图\n",
    "f, ax = plt.subplots(1)\n",
    "f.set_figheight(6)\n",
    "f.set_figwidth(15)\n",
    "# 画出拟合的图像并显示误差\n",
    "sns.lineplot(x=time.flatten(), y=power, ax=ax, label='True')\n",
    "sns.lineplot(x=time.flatten(), y=power_predict, ax=ax, label='Predict')\n",
    "plt.title(f'Linear Regression\\nMAE: {mae}')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Power')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58e47c47-151a-4d32-bb3e-635d3c1bd32e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     年份  石油可供量(万吨)  石油生产量(万吨)  进口石油量(万吨)  出口石油量(-)(万吨)  年初年末石油库存差额(万吨)  \\\n",
      "0  2021    69048.0    19888.1    58820.0        7616.8         -2043.2   \n",
      "1  2020    67553.7    19476.9    61271.7        7551.0         -5643.8   \n",
      "2  2019    66900.9    19101.4    58102.2        8211.4         -2091.4   \n",
      "3  2018    63726.6    18932.4    54094.3        7557.4         -1742.7   \n",
      "4  2017    60810.8    19150.6    49141.2        7026.7          -454.3   \n",
      "\n",
      "   石油能源消费总量(万吨)  农、林、牧、渔业石油消费总量(万吨)  工业石油消费总量(万吨)  建筑业石油消费总量(万吨)  ...  \\\n",
      "0       68393.4              1981.0       28218.9         4270.1  ...   \n",
      "1       65369.1              1773.1       27711.1         4180.3  ...   \n",
      "2       64506.5              1748.2       25210.6         4055.1  ...   \n",
      "3       62245.1              1724.9       22460.3         3935.7  ...   \n",
      "4       60395.9              1786.4       21486.7         3803.5  ...   \n",
      "\n",
      "   居民生活石油消费总量(万吨)  石油终端消费量(万吨)  工业终端石油消费量(万吨)  中间消费(用于加工转换)(万吨)  \\\n",
      "0          7504.6      65630.4        25456.9            2743.0   \n",
      "1          7170.1      62085.2        24428.4            3265.6   \n",
      "2          7314.0      61018.5        21732.4            3453.6   \n",
      "3          7328.4      58623.0        18847.6            3593.3   \n",
      "4          7139.7      56880.0        17980.5            3468.9   \n",
      "\n",
      "   火力发电中间消费石油(万吨)  供热中间消费石油(万吨)  制气中间消费石油(万吨)  炼油损失量(万吨)  石油损失量(万吨)  \\\n",
      "0           324.2         677.6          60.5     1680.7       20.0   \n",
      "1           321.5         677.7          28.9     2232.9       18.3   \n",
      "2           308.3         653.8           3.7     2487.9       34.5   \n",
      "3           309.3         590.2           4.7     2689.0       28.8   \n",
      "4           280.6         522.6           4.7     2661.0       47.0   \n",
      "\n",
      "   石油平衡差额(万吨)  \n",
      "0       654.6  \n",
      "1      2184.6  \n",
      "2      2394.3  \n",
      "3      1481.5  \n",
      "4       414.9  \n",
      "\n",
      "[5 rows x 23 columns]\n",
      "DatetimeIndex(['1970-01-01 00:00:00.000002020',\n",
      "               '1970-01-01 00:00:00.000002020',\n",
      "               '1970-01-01 00:00:00.000002019',\n",
      "               '1970-01-01 00:00:00.000002018',\n",
      "               '1970-01-01 00:00:00.000002017',\n",
      "               '1970-01-01 00:00:00.000002015',\n",
      "               '1970-01-01 00:00:00.000002015',\n",
      "               '1970-01-01 00:00:00.000002014',\n",
      "               '1970-01-01 00:00:00.000002013',\n",
      "               '1970-01-01 00:00:00.000002012',\n",
      "               '1970-01-01 00:00:00.000002011',\n",
      "               '1970-01-01 00:00:00.000002009',\n",
      "               '1970-01-01 00:00:00.000002009',\n",
      "               '1970-01-01 00:00:00.000002008',\n",
      "               '1970-01-01 00:00:00.000002007',\n",
      "               '1970-01-01 00:00:00.000002006',\n",
      "               '1970-01-01 00:00:00.000002004',\n",
      "               '1970-01-01 00:00:00.000002004'],\n",
      "              dtype='datetime64[ns]', freq=None)\n",
      "y = 90137770897.83318x + -161831.75837633375\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              石油生产量(万吨)   R-squared:                       0.772\n",
      "Model:                            OLS   Adj. R-squared:                  0.723\n",
      "Method:                 Least Squares   F-statistic:                     15.77\n",
      "Date:                Sun, 14 Apr 2024   Prob (F-statistic):           9.09e-05\n",
      "Time:                        20:51:51   Log-Likelihood:                -137.66\n",
      "No. Observations:                  18   AIC:                             283.3\n",
      "Df Residuals:                      14   BIC:                             286.9\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       1.139e+04   1481.035      7.692      0.000    8216.204    1.46e+04\n",
      "x1            -0.1582      0.084     -1.894      0.079      -0.337       0.021\n",
      "x2            -0.8860      0.361     -2.456      0.028      -1.660      -0.112\n",
      "x3             0.3701      0.071      5.187      0.000       0.217       0.523\n",
      "==============================================================================\n",
      "Omnibus:                        0.509   Durbin-Watson:                   0.884\n",
      "Prob(Omnibus):                  0.775   Jarque-Bera (JB):                0.583\n",
      "Skew:                          -0.160   Prob(JB):                        0.747\n",
      "Kurtosis:                       2.179   Cond. No.                     6.99e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 6.99e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\Lib\\site-packages\\scipy\\stats\\_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a4b582a550>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 引入必要的库包\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "os.chdir(r'C:\\Users\\A\\Desktop')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('Petroleum_20_bfill.csv', encoding='utf-8')\n",
    "print(data.head())\n",
    "# 选取数据中的时间和生产量两列\n",
    "time = data['年份']\n",
    "power = data['石油生产量(万吨)']\n",
    "\n",
    "# 用线性拟合的库包进行拟合\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# 将时间转换为时间戳\n",
    "time = pd.to_datetime(time)\n",
    "time = (time - pd.to_datetime('1970-01-01')).dt.total_seconds().values\n",
    "time = time.reshape(-1, 1)\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "# 拟合数据\n",
    "model.fit(time, power)\n",
    "# 预测数据\n",
    "power_predict = model.predict(time)\n",
    "# 将时间变回原来的格式\n",
    "time = pd.to_datetime(time[:, 0], unit='s')\n",
    "print(time)\n",
    "# 给出拟合的函数\n",
    "print('y = {}x + {}'.format(model.coef_[0], model.intercept_))\n",
    "# 计算拟合的误差\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mae =  mean_squared_error(power, power_predict)\n",
    "plt.figure(figsize=(20, 8))\n",
    "# 画出拟合的图像并显示误差\n",
    "plt.plot(time, power, label='True')\n",
    "plt.plot(time, power_predict, label='Predict')\n",
    "plt.title('Linear Regression\\nMAE:{}'.format(mae))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Power')\n",
    "# 设置图像大小\n",
    "plt.legend()\n",
    "plt.show()\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# 选取数据中的特征变量和目标变量\n",
    "target = data[\"石油生产量(万吨)\"]\n",
    "features = data[[\"进口石油量(万吨)\",\"出口石油量(-)(万吨)\",\"石油能源消费总量(万吨)\"]]\n",
    "\n",
    "# 将特征变量转换为numpy数组\n",
    "X = np.array(features)\n",
    "\n",
    "# 添加一个常数项，对应截距项\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# 创建线性回归模型\n",
    "model = sm.OLS(target, X).fit()\n",
    "\n",
    "# 打印出回归结果报告\n",
    "print(model.summary())\n",
    "# 用model.predict()函数进行预测\n",
    "predict = model.predict(X)\n",
    "\n",
    "# 计算预测值和真实值之间的均方误差\n",
    "mae = mean_squared_error(target, predict)\n",
    "plt.figure(figsize=(20, 8))\n",
    "# 画出真实值和预测值的对比图\n",
    "plt.plot(time, target, label='True')\n",
    "plt.plot(time, predict, label='Predict')\n",
    "plt.title('Multiple Linear Regression\\nMAE:{}'.format(mae))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Power')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a373bf67-1001-42ed-9f76-6e1fad65dbd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#天然气"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a055312-f184-4078-9ca1-d0820cb071d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     年份  天然气可供量(亿立方米)  天然气生产量(亿立方米)  进口天然气量(亿立方米)  出口天然气量(-)(亿立方米)  \\\n",
      "0  2021        3773.8        2155.5        1673.5             55.2   \n",
      "1  2020        3340.2        1994.9        1397.0             51.7   \n",
      "2  2019        3057.5        1761.7        1331.8             36.1   \n",
      "3  2018        2814.3        1601.6        1246.4             33.6   \n",
      "4  2017        2390.7        1480.4         945.6             35.3   \n",
      "\n",
      "   年初年末天然气库存差额(亿立方米)  天然气能源消费总量(亿立方米)  农、林、牧、渔业天然气消费总量(亿立方米)  工业天然气消费总量(亿立方米)  \\\n",
      "0                0.0           3773.0                    1.7           2678.2   \n",
      "1                0.0           3339.9                    1.3           2304.0   \n",
      "2                0.0           3059.7                    1.2           2092.1   \n",
      "3                0.0           2817.1                    1.3           1940.1   \n",
      "4                0.0           2393.7                    1.1           1575.2   \n",
      "\n",
      "   建筑业天然气消费总量(亿立方米)  交通运输、仓储和邮政业天然气消费总量(亿立方米)  批发和零售业、住宿和餐饮业天然气消费总量(亿立方米)  \\\n",
      "0               3.2                     366.3                        70.2   \n",
      "1               2.6                     354.3                        62.1   \n",
      "2               2.8                     341.5                        62.5   \n",
      "3               2.5                     286.2                        60.8   \n",
      "4               1.8                     284.7                        57.6   \n",
      "\n",
      "   其他天然气消费总量(亿立方米)  居民生活天然气消费总量(亿立方米)  天然气平衡差额(亿立方米)  \n",
      "0             61.0              592.3            0.8  \n",
      "1             55.6              560.0            0.3  \n",
      "2             57.3              502.3           -2.2  \n",
      "3             57.9              468.4           -2.8  \n",
      "4             52.9              420.3           -3.0  \n",
      "DatetimeIndex(['1970-01-01 00:00:00.000002020',\n",
      "               '1970-01-01 00:00:00.000002020',\n",
      "               '1970-01-01 00:00:00.000002019',\n",
      "               '1970-01-01 00:00:00.000002018',\n",
      "               '1970-01-01 00:00:00.000002017',\n",
      "               '1970-01-01 00:00:00.000002015',\n",
      "               '1970-01-01 00:00:00.000002015',\n",
      "               '1970-01-01 00:00:00.000002014',\n",
      "               '1970-01-01 00:00:00.000002013',\n",
      "               '1970-01-01 00:00:00.000002012',\n",
      "               '1970-01-01 00:00:00.000002011',\n",
      "               '1970-01-01 00:00:00.000002009',\n",
      "               '1970-01-01 00:00:00.000002009',\n",
      "               '1970-01-01 00:00:00.000002008',\n",
      "               '1970-01-01 00:00:00.000002007',\n",
      "               '1970-01-01 00:00:00.000002006',\n",
      "               '1970-01-01 00:00:00.000002004',\n",
      "               '1970-01-01 00:00:00.000002004'],\n",
      "              dtype='datetime64[ns]', freq=None)\n",
      "y = 92785655314.75775x + -185554.58132095\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "MissingDataError",
     "evalue": "exog contains inf or nans",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMissingDataError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 60\u001b[0m\n\u001b[0;32m     57\u001b[0m X \u001b[38;5;241m=\u001b[39m sm\u001b[38;5;241m.\u001b[39madd_constant(X)\n\u001b[0;32m     59\u001b[0m \u001b[38;5;66;03m# 创建线性回归模型\u001b[39;00m\n\u001b[1;32m---> 60\u001b[0m model \u001b[38;5;241m=\u001b[39m sm\u001b[38;5;241m.\u001b[39mOLS(target, X)\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     62\u001b[0m \u001b[38;5;66;03m# 打印出回归结果报告\u001b[39;00m\n\u001b[0;32m     63\u001b[0m \u001b[38;5;28mprint\u001b[39m(model\u001b[38;5;241m.\u001b[39msummary())\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\statsmodels\\regression\\linear_model.py:922\u001b[0m, in \u001b[0;36mOLS.__init__\u001b[1;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m    919\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWeights are not supported in OLS and will be ignored\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    920\u001b[0m            \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn exception will be raised in the next version.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    921\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(msg, ValueWarning)\n\u001b[1;32m--> 922\u001b[0m \u001b[38;5;28msuper\u001b[39m(OLS, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(endog, exog, missing\u001b[38;5;241m=\u001b[39mmissing,\n\u001b[0;32m    923\u001b[0m                           hasconst\u001b[38;5;241m=\u001b[39mhasconst, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    924\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_keys:\n\u001b[0;32m    925\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_keys\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\statsmodels\\regression\\linear_model.py:748\u001b[0m, in \u001b[0;36mWLS.__init__\u001b[1;34m(self, endog, exog, weights, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m    746\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    747\u001b[0m     weights \u001b[38;5;241m=\u001b[39m weights\u001b[38;5;241m.\u001b[39msqueeze()\n\u001b[1;32m--> 748\u001b[0m \u001b[38;5;28msuper\u001b[39m(WLS, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(endog, exog, missing\u001b[38;5;241m=\u001b[39mmissing,\n\u001b[0;32m    749\u001b[0m                           weights\u001b[38;5;241m=\u001b[39mweights, hasconst\u001b[38;5;241m=\u001b[39mhasconst, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    750\u001b[0m nobs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    751\u001b[0m weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\statsmodels\\regression\\linear_model.py:202\u001b[0m, in \u001b[0;36mRegressionModel.__init__\u001b[1;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[0;32m    201\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, endog, exog, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 202\u001b[0m     \u001b[38;5;28msuper\u001b[39m(RegressionModel, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(endog, exog, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    203\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpinv_wexog: Float64Array \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    204\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_attr\u001b[38;5;241m.\u001b[39mextend([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpinv_wexog\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwendog\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwexog\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\statsmodels\\base\\model.py:270\u001b[0m, in \u001b[0;36mLikelihoodModel.__init__\u001b[1;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[0;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, endog, exog\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 270\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(endog, exog, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    271\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitialize()\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\statsmodels\\base\\model.py:95\u001b[0m, in \u001b[0;36mModel.__init__\u001b[1;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[0;32m     93\u001b[0m missing \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmissing\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     94\u001b[0m hasconst \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhasconst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m---> 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_data(endog, exog, missing, hasconst,\n\u001b[0;32m     96\u001b[0m                               \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk_constant \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mk_constant\n\u001b[0;32m     98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mexog\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\statsmodels\\base\\model.py:135\u001b[0m, in \u001b[0;36mModel._handle_data\u001b[1;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m    134\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_handle_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, endog, exog, missing, hasconst, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 135\u001b[0m     data \u001b[38;5;241m=\u001b[39m handle_data(endog, exog, missing, hasconst, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    136\u001b[0m     \u001b[38;5;66;03m# kwargs arrays could have changed, easier to just attach here\u001b[39;00m\n\u001b[0;32m    137\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m kwargs:\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\statsmodels\\base\\data.py:675\u001b[0m, in \u001b[0;36mhandle_data\u001b[1;34m(endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m    672\u001b[0m     exog \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(exog)\n\u001b[0;32m    674\u001b[0m klass \u001b[38;5;241m=\u001b[39m handle_data_class_factory(endog, exog)\n\u001b[1;32m--> 675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m klass(endog, exog\u001b[38;5;241m=\u001b[39mexog, missing\u001b[38;5;241m=\u001b[39mmissing, hasconst\u001b[38;5;241m=\u001b[39mhasconst,\n\u001b[0;32m    676\u001b[0m              \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\statsmodels\\base\\data.py:88\u001b[0m, in \u001b[0;36mModelData.__init__\u001b[1;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconst_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     87\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk_constant \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m---> 88\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_constant(hasconst)\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_integrity()\n\u001b[0;32m     90\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache \u001b[38;5;241m=\u001b[39m {}\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\statsmodels\\base\\data.py:134\u001b[0m, in \u001b[0;36mModelData._handle_constant\u001b[1;34m(self, hasconst)\u001b[0m\n\u001b[0;32m    132\u001b[0m exog_max \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmax(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(exog_max)\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m--> 134\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MissingDataError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexog contains inf or nans\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    135\u001b[0m exog_min \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m    136\u001b[0m const_idx \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mwhere(exog_max \u001b[38;5;241m==\u001b[39m exog_min)[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39msqueeze()\n",
      "\u001b[1;31mMissingDataError\u001b[0m: exog contains inf or nans"
     ]
    }
   ],
   "source": [
    "# 引入必要的库包\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "os.chdir(r'C:\\Users\\A\\Desktop')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('Natural_gas_20_bfill.csv', encoding='utf-8')\n",
    "print(data.head())\n",
    "# 选取数据中的时间和生产量两列\n",
    "time = data['年份']\n",
    "power = data['天然气生产量(亿立方米)']\n",
    "\n",
    "# 用线性拟合的库包进行拟合\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# 将时间转换为时间戳\n",
    "time = pd.to_datetime(time)\n",
    "time = (time - pd.to_datetime('1970-01-01')).dt.total_seconds().values\n",
    "time = time.reshape(-1, 1)\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "# 拟合数据\n",
    "model.fit(time, power)\n",
    "# 预测数据\n",
    "power_predict = model.predict(time)\n",
    "# 将时间变回原来的格式\n",
    "time = pd.to_datetime(time[:, 0], unit='s')\n",
    "print(time)\n",
    "# 给出拟合的函数\n",
    "print('y = {}x + {}'.format(model.coef_[0], model.intercept_))\n",
    "# 计算拟合的误差\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mae =  mean_squared_error(power, power_predict)\n",
    "plt.figure(figsize=(20, 8))\n",
    "# 画出拟合的图像并显示误差\n",
    "plt.plot(time, power, label='True')\n",
    "plt.plot(time, power_predict, label='Predict')\n",
    "plt.title('Linear Regression\\nMAE:{}'.format(mae))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Power')\n",
    "# 设置图像大小\n",
    "plt.legend()\n",
    "plt.show()\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# 选取数据中的特征变量和目标变量\n",
    "target = data[\"天然气生产量(亿立方米)\"]\n",
    "features = data[[\"进口天然气量(亿立方米)\",\"出口天然气量(-)(亿立方米)\",\"天然气能源消费总量(亿立方米)\"]]\n",
    "\n",
    "# 将特征变量转换为numpy数组\n",
    "X = np.array(features)\n",
    "\n",
    "# 添加一个常数项，对应截距项\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# 创建线性回归模型\n",
    "model = sm.OLS(target, X).fit()\n",
    "\n",
    "# 打印出回归结果报告\n",
    "print(model.summary())\n",
    "# 用model.predict()函数进行预测\n",
    "predict = model.predict(X)\n",
    "\n",
    "# 计算预测值和真实值之间的均方误差\n",
    "mae = mean_squared_error(target, predict)\n",
    "plt.figure(figsize=(20, 8))\n",
    "# 画出真实值和预测值的对比图\n",
    "plt.plot(time, target, label='True')\n",
    "plt.plot(time, predict, label='Predict')\n",
    "plt.title('Multiple Linear Regression\\nMAE:{}'.format(mae))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Power')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e2f411-b8a5-49d4-bbaf-a434539c73df",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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